{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6f8436be",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "import numpy as np\n",
    "import yfinance as yf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.layers import LSTM, Dense, Dropout, Conv1D, MaxPooling1D, Flatten\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score  # 导入评估指标函数\n",
    "import matplotlib.pyplot as plt  # 导入数据可视化库 Matplotlib\n",
    "import yfinance as yf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import pickle\n",
    "from tqdm.notebook import tnrange\n",
    "import seaborn as sns\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62504e3e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd625d5b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b212ade",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale train and test data to new feature range[0, 1]将训练和测试数据缩放到新的特征范围[0，1]\n",
    "def scale(train, test):\n",
    "\t# fit scaler\n",
    "\tscaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\tscaler = scaler.fit(train)\n",
    "\t# transform train\n",
    "\ttrain = train.reshape(train.shape[0], train.shape[1])\n",
    "\ttrain_scaled = scaler.transform(train)\n",
    "\t# transform test\n",
    "\ttest = test.reshape(test.shape[0], test.shape[1])\n",
    "\ttest_scaled = scaler.transform(test)\n",
    "\treturn scaler, train_scaled, test_scaled\n",
    "\n",
    "# compute wMAPE weighted absolute percentage error计算wMAPE加权绝对百分比误差\n",
    "def wMAPE(actual, predicted): \n",
    "    result_nom = 0\n",
    "    result_deno = 0\n",
    "    for i in range(len(actual)):\n",
    "        result_nom +=  abs(actual[i] - predicted[i])\n",
    "        result_deno +=  abs(actual[i]) \n",
    "    result = result_nom/result_deno\n",
    "    return result *100\n",
    "\n",
    "def scaled(X, y):\n",
    "    max_train = np.max(X, axis=0) #标准化\n",
    "    min_train = np.min(X, axis=0)\n",
    "    X = 0.0 + (X - min_train) / (max_train - min_train)\n",
    "    max_targets = np.max(y, axis=0)\n",
    "    min_targets = np.min(y, axis=0)\n",
    "    y = 0.0 + (y - min_targets) / (max_targets - min_targets)\n",
    "    return X, y, max_train, min_train, max_targets, min_targets\n",
    "\n",
    "def inscaled(ypred):\n",
    "    y_pred = ypred * (max_targets - min_targets) + min_targets\n",
    "    return y_pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "74b5554c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       radiation  air temperature  air pressure  humidity  actual power/MW\n",
      "0            0.0            -14.1       908.672    81.997              0.0\n",
      "1            0.0            -14.6       908.672    82.597              0.0\n",
      "2            0.0            -14.4       908.672    82.897              0.0\n",
      "3            0.0            -14.8       908.672    82.997              0.0\n",
      "4            0.0            -14.8       909.172    82.897              0.0\n",
      "...          ...              ...           ...       ...              ...\n",
      "35021        0.0             -4.8       899.600    54.900              0.0\n",
      "35022        0.0             -5.9       900.100    55.800              0.0\n",
      "35023        0.0             -6.5       900.100    57.900              0.0\n",
      "35024        0.0             -7.4       900.100    59.400              0.0\n",
      "35025        0.0             -8.0       900.100    62.700              0.0\n",
      "\n",
      "[35026 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df = pd.read_csv('PVDATA.csv')\n",
    "# 删除时间列\n",
    "df = df.drop(columns=['TIME'])\n",
    "# 目标列\n",
    "target_column = 'actual power/MW'\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5dc78b01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.    -14.1   908.672  81.997]\n",
      " [  0.    -14.6   908.672  82.597]\n",
      " [  0.    -14.4   908.672  82.897]\n",
      " ...\n",
      " [  0.     -6.5   900.1    57.9  ]\n",
      " [  0.     -7.4   900.1    59.4  ]\n",
      " [  0.     -8.    900.1    62.7  ]]\n"
     ]
    }
   ],
   "source": [
    "X = df.drop(columns=target_column).values  # 从 DataFrame `df` 中去除目标列 `target_column`，并将剩余的列转换为数组赋值给变量 `X`\n",
    "\n",
    "y = df[target_column].values  # 从 DataFrame `df` 中选择目标列 `target_column`，并将其转换为数组赋值给变量 `y`\n",
    "print(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "079a903b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.          0.70982043 -0.1581825  -0.25278188  0.63564631]\n",
      " [ 0.70982043  1.         -0.59307447 -0.14555079  0.57492931]\n",
      " [-0.1581825  -0.59307447  1.         -0.0128098  -0.2217157 ]\n",
      " [-0.25278188 -0.14555079 -0.0128098   1.         -0.29023335]\n",
      " [ 0.63564631  0.57492931 -0.2217157  -0.29023335  1.        ]]\n"
     ]
    }
   ],
   "source": [
    "X, y, max_train, min_train, max_targets, min_targets = scaled(X, y)#标准化\n",
    "# 计算皮尔逊相关系数矩阵\n",
    "corr = np.corrcoef(X,y,rowvar=False)\n",
    "print(corr)\n",
    "# 计算每个特征的相关性权重\n",
    "weights = np.abs(corr[-1, :-1])\n",
    "# 对数据进行加权\n",
    "X = np.multiply(X, weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bfa2bf71",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 3000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_names = [ 'radiation','air temperature', 'air pressure','humidity','actual power/MW']\n",
    "plt.figure(figsize=(30,20))\n",
    "sns.set(font_scale=1.2)\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f', annot_kws={\"size\": 12},\n",
    "            xticklabels=feature_names, yticklabels=feature_names,)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bfef9bab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train X shape: (28020,)\n"
     ]
    }
   ],
   "source": [
    "dataset = y\n",
    "train_size = int(len(dataset) * 0.8)  # 计算训练集大小，将数据集长度的 80% 转换为整数\n",
    "test_size = len(dataset) - train_size  # 计算测试集大小，将剩余的数据作为测试集\n",
    "train, test = dataset[0: train_size], dataset[train_size: len(dataset)]  # 将数据集划分为训练集和测试集，使用切片操作实现\n",
    "print(\"Train X shape:\", train.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e3de8f1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c4e60c54",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''定义一个函数用于创建数据集，输入参数包括:\n",
    "dataset: 数据集\n",
    "look_back: 回溯长度，即用多少个时间步作为输入预测下一个时间步'''\n",
    "def creat_dataset(dataset, look_back):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset) - look_back - 1):# 循环遍历数据集\n",
    "        a = dataset[i: (i + look_back)]# 提取回溯长度的数据作为输入\n",
    "        dataX.append(a)# 将该输入数据添加到输入数据集\n",
    "        dataY.append(dataset[i + look_back])# 将下一个时间步的数据添加到输出数据集\n",
    "    return np.array(dataX), np.array(dataY)# 将输入输出数据集转换为numpy数组并返回"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e15085c",
   "metadata": {},
   "outputs": [],
   "source": [
    "look_back = 2\n",
    "train_X, train_Y = creat_dataset(train, look_back)  # 根据训练集数据和滑动窗口大小创建训练集的输入序列和输出值\n",
    "test_X, test_Y = creat_dataset(test, look_back)  # 根据测试集数据和滑动窗口大小创建测试集的输入序列和输出值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fc162762",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train X shape: (28017, 2)\n",
      "Train Y shape: (28017,)\n",
      "Test X shape: (7003, 2)\n",
      "Test Y shape: (7003,)\n"
     ]
    }
   ],
   "source": [
    "# Ensure the sizes of training and testing sets\n",
    "print(\"Train X shape:\", train_X.shape)\n",
    "print(\"Train Y shape:\", train_Y.shape)\n",
    "print(\"Test X shape:\", test_X.shape)\n",
    "print(\"Test Y shape:\", test_Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7fac2c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 调用GPU加速\n",
    "gpus = tf.config.experimental.list_physical_devices(device_type='GPU')\n",
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "482a57a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint , ReduceLROnPlateau\n",
    "from tensorflow.keras.layers import Conv1D, LSTM, Bidirectional ,ZeroPadding1D ,BatchNormalization, Add,Layer,Dot\n",
    "# from keras.layers.embeddings import Embedding\n",
    "save_best = ModelCheckpoint(\"new_weights.h5\", monitor='val_loss', save_best_only=True, save_weights_only=True)\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.25,patience=4, min_lr=0.00001,verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "67b01cf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "438/438 [==============================] - 4s 6ms/step - loss: 0.0171 - val_loss: 0.0040 - lr: 0.0010\n",
      "Epoch 2/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0062 - val_loss: 0.0039 - lr: 0.0010\n",
      "Epoch 3/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0059 - val_loss: 0.0034 - lr: 0.0010\n",
      "Epoch 4/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0055 - val_loss: 0.0032 - lr: 0.0010\n",
      "Epoch 5/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0053 - val_loss: 0.0029 - lr: 0.0010\n",
      "Epoch 6/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0052 - val_loss: 0.0029 - lr: 0.0010\n",
      "Epoch 7/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0052 - val_loss: 0.0029 - lr: 0.0010\n",
      "Epoch 8/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0051 - val_loss: 0.0030 - lr: 0.0010\n",
      "Epoch 9/50\n",
      "436/438 [============================>.] - ETA: 0s - loss: 0.0051\n",
      "Epoch 9: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0051 - val_loss: 0.0031 - lr: 0.0010\n",
      "Epoch 10/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 2.5000e-04\n",
      "Epoch 11/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 2.5000e-04\n",
      "Epoch 12/50\n",
      "438/438 [==============================] - 2s 6ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 2.5000e-04\n",
      "Epoch 13/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 2.5000e-04\n",
      "Epoch 14/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0029 - lr: 2.5000e-04\n",
      "Epoch 15/50\n",
      "436/438 [============================>.] - ETA: 0s - loss: 0.0049\n",
      "Epoch 15: ReduceLROnPlateau reducing learning rate to 6.25000029685907e-05.\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 2.5000e-04\n",
      "Epoch 16/50\n",
      "438/438 [==============================] - 3s 7ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 17/50\n",
      "438/438 [==============================] - 3s 6ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 18/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 19/50\n",
      "438/438 [==============================] - ETA: 0s - loss: 0.0049\n",
      "Epoch 19: ReduceLROnPlateau reducing learning rate to 1.5625000742147677e-05.\n",
      "438/438 [==============================] - 3s 6ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 6.2500e-05\n",
      "Epoch 20/50\n",
      "438/438 [==============================] - 3s 6ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 21/50\n",
      "438/438 [==============================] - 3s 6ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 22/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 23/50\n",
      "432/438 [============================>.] - ETA: 0s - loss: 0.0048\n",
      "Epoch 23: ReduceLROnPlateau reducing learning rate to 1e-05.\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.5625e-05\n",
      "Epoch 24/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 25/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 26/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 27/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 28/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 29/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 30/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 31/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 32/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 33/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 34/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 35/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 36/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 37/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 38/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 39/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 40/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 41/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 42/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 43/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 44/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 45/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0049 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 46/50\n",
      "438/438 [==============================] - 2s 6ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 47/50\n",
      "438/438 [==============================] - 2s 6ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 48/50\n",
      "438/438 [==============================] - 2s 6ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 49/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n",
      "Epoch 50/50\n",
      "438/438 [==============================] - 2s 5ms/step - loss: 0.0048 - val_loss: 0.0028 - lr: 1.0000e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x22cb2e5e7c0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.layers import Concatenate, Input\n",
    "from keras.models import Model\n",
    "lstm_units = 128\n",
    "inputs=Input(shape=(train_X.shape[1], 1))\n",
    "\n",
    "x = Conv1D(filters = 128, kernel_size = 1, activation = 'relu')(inputs)  #, padding = 'same'\n",
    "x = Dropout(0.3)(x)\n",
    "\n",
    "#lstm_out = Bidirectional(LSTM(lstm_units, activation='relu'), name='bilstm')(x)\n",
    "#对于GPU可以使用CuDNNLSTM\n",
    "lstm_out = Bidirectional(LSTM(lstm_units, return_sequences=True))(x)\n",
    "lstm_out = Dropout(0.3)(lstm_out)\n",
    "\n",
    "attention_mul = Flatten()(lstm_out)\n",
    "\n",
    "output = Dense(1, activation='sigmoid')(attention_mul)\n",
    "model = Model(inputs=[inputs], outputs=output)\n",
    "\n",
    "# Compile and train the CNN model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "model.fit(train_X, train_Y, epochs=50, batch_size=64,validation_data=(test_X , test_Y),\n",
    "            callbacks=[reduce_lr , save_best])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cdd2fb0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "876/876 [==============================] - 1s 1ms/step\n",
      "219/219 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "# 对训练集进行预测\n",
    "ypred_train = model.predict(train_X)\n",
    "# 将训练集的预测结果转换为原始数据的范围\n",
    "ypred_train = inscaled(ypred_train)\n",
    "# 将训练集的真实标签转换为原始数据的范围\n",
    "y_train = inscaled(train_Y)\n",
    "\n",
    "# 对测试集进行预测\n",
    "ypred_test = model.predict(test_X)\n",
    "# 将测试集的预测结果转换为原始数据的范围\n",
    "ypred_test = inscaled(ypred_test)\n",
    "# 将测试集的真实标签转换为原始数据的范围\n",
    "y_test = inscaled(test_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "84f61209",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_test[0:len(ypred_test)-1]\n",
    "ypred_test = ypred_test[1:len(ypred_test),0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cbde8d05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. ... 0. 0. 0.]\n",
      "RMSE 0.466 \n",
      "MAE 0.274 \n",
      "R2 0.995 \n",
      "wMAPE 7.570 \n"
     ]
    }
   ],
   "source": [
    "print(y_test)\n",
    "# 计算测试集的均方根误差（RMSE）\n",
    "testScore = math.sqrt(mean_squared_error(y_test, ypred_test))\n",
    "print('RMSE %.3f ' %(testScore))\n",
    "# 计算测试集的平均绝对误差（MAE）\n",
    "testScore = mean_absolute_error(y_test, ypred_test)\n",
    "print('MAE %.3f ' %(testScore))\n",
    "# 计算测试集的R平方值（R2）\n",
    "testScore = r2_score(y_test, ypred_test)\n",
    "print('R2 %.3f ' %(testScore))\n",
    "testScore = wMAPE(y_test, ypred_test)\n",
    "print('wMAPE %.3f ' %(testScore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d0954a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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06qlnMGPGrM6fd+3ayWOPPcyRRy5g/vwFndvdXxu+KIQQQuyNkkjQ8N//YB49huw5h1PbHODJd7cyf2ox08d0FJk7dEwu+S4zf/zPSu59fi0/O2s+1jWraX75f2TPnI1GCr0KQaSqkoYnHie0ZTP2eUdS8L2L1Q5JCDHAJFkXnSZOnMzEiZM7f165cgWPPfYw5eWjOP74E1WMTAghRLpq/2Qp0doahl9/IwCPvbkZtz2LcxaOQqv9shBnca6NK8+czH3/W0dDaxDPaWdQedNvaV/6MY6581SKXgj1Jfx+mv73PN7338OYX4Bz4TG0LXoH27RDsU2eqnZ4QqQMRVEybrlt1ZP1tWvXcs8997Bq1SoikQjl5eVceOGFnHbaaZ37hMNh7r33Xl599VVaWloYN24cV111FXPmzFEvcCGEEELskxKP0/Ly/7BNm07WiBF8trmRTZVtXH3OVIyG7r3lY0qc/PmHczDodSSTVmzTZ9D88ovYD5uDRq/6VxYhBk20rpbml14k3tpKZHcVALnfPgfnwqNBpyPa0ED9ow8TP/V0ort3Y506DeshE1SOWgh1RKqqaPvgPXzLlpF75ll4Fi7Y/5PShKpVW7Zv384FF1zA5s2bueSSS/jFL36B2Wzm2muv5eGHH+7c7+qrr+Zf//oXRx99NNdeey2xWIxLLrmEFStWqBj90LVy5Qrmzp3BK6+8yMUXf4eFCw/nmmuupLa2hrlzZ3Dfffd02T8SiTB37gxuueXGLts3bFjH1VdfyXHHzeeYY+Zy+eWXsWLFp4P4mwghhBhI3o+WEGtuwnPqaQBMG53Dz8+awoQRe59OZdDrSCST3P3cWrZNXEi8uRn/6pWDFLEQ6ktGItT87R5C27ehz8nBecxxjPj9rbiOOx6NXo9Go6HgwotQ4nEaHnsU70cf0ixLHoohKN7WRt2/HqTidzfgX7kS51ELsE2foXZY/UrV29R/+tOf0Gq1PPPMM+Tn5wNw/vnnc95553H33Xdz1llnsXbtWt555x2uu+46LrzwQgBOO+00TjnlFP7whz/w/PPy5qSWu+76CwsXHstJJ52K2Wzu03M/+2w511xzJcOHj+Ciiy4F4O23X+fnP7+c3/3uDyxYcMxAhCyEEGKQKPE4La+8RPbMWZiGldDQGiTPZWHiSM9+n6vTarFmGXhieSOXFJbh2LiB7K/UVBEikzU88R9izU2U3nAjxsKiLo8pikJdS5Ach52yW/6ERq/Hv/Iz6v71T+JtbeidTnWCFmKQJaNRKn73GxQlSd4F38Mx98iMrG+iWrKeSCRYvnw58+bN60zUAbRaLd/4xjdYtWoVGzdu5OWXX8ZgMHDWWWd17mOxWDjzzDP561//yq5duxgxYoQKv0GHaGMDyWBwQM+h02lJJJK92ldrsWDMzRvQePYYNWoM1133m86fa2trevW8ZDLJn/98CyNHjuIf/3gY/RdDG88882x+8pNLufPO25g7dz4GqXIqhBBpy/fZcuKtLbi/eQobdrXwlydXc913pjNqmKNXz//OcWPYXu3lf8psvrvpffL3/xQh0l7bhx/SvuRD8i/8frdEvckb4qFXNrK5qo18t4ULTxjL2OE2rJOngFaLf81qnPOPUidwIQZZMhgk4Wun6PKfYps6Te1wBoxqybpWq+Wll17qsQhAS0sLADqdjnXr1lFWVobFYumyz4QJHfNy1q1bp1qynvD52PWra0FRVDl/j7Rayv9yF7rs7AE/1fTpMw/oeVu3bqG6ejeXXPJD/H5/l8fmzTuKf/zjXjZt2sCkSVP6I0whhBAqaHv3bSzjD0FXUMhjDy5jdImT8mJ7r59vNum57JQJ3PLvFawJWihtacEgq5GIDNa8bDk1Dz+Efd6R2I+YC0AgHGNLVRvTRudiMxvQajVcfOJ4Plhbw5/+u4qzFozihNnDMY8Zi3/VSknWxZChJBMAaL7o3EsqCk1tIfJcln09Le2olqxrNBpKSkq6bQ8Ggzz33HNYLBYOOeQQ6uvrmTx5crf98vI6eo9ranrXmzsQdNnZjPjDn1KuZ30wEnUAl+vAvjTt/qJQyoMP3s+DD97f4z51dbWSrAshRJoK7dhOeMcOii7/KR+uraWhNcSPT5/U5yq9I4vsTB/lItGgI7R5I4Y5RwxQxEKoK7BxI5V3/IXsQ6eTf8GFxOJJ3lhWyZvLq9AAf73CQ5ZRz/+d29GDePikApauq6MoxwqAftKh+F94ikQohK6PUxOFSEdK4otkXavl43W1/G/JTiKxJLf/+HD0elXLsvWrlCqtqigK119/PY2NjfzkJz/BZDIRCAR6nA+dlZUFQCgUOqhz7qsxk8n9f6kY6CHnGs2XyXoqdeBDR1y9kUwmv/Zzx8V14YWXMGVKz8NWysrKDy64Qbbn+6dG032ghU6nyag3jXS25zXb29euUIe0U3rYVzu1v/cuhtxcsiZN5uX7ljJnYgFlRb3vVf+qK8+Zzvb1LxDavAn3PFnCra/kekp9sbY2dv/9b9jHj2PYj39MTNHw9+fWsHFXKwunF/PNw0dgzur+lX3+tGIAqpsC/H69gdP1boo2rsMxa/Zg/wpDhlxPqSOh6fjCrTXoeeSVTYwd7uKM+SMxGXUZ1U4pk6wrisKNN97Iq6++yqxZs/jRj37Uq+cdzFp6Wq0Gl8u618fDYR1NTdqUSLbUeLHtOadW2/X3/3K7tst2o7Hj5ZRIxLps93o7pjVoNB3HGTas48PFbM7qtvzetm1bqa+vw2Yzq/43PxBfbadkUoNWq8XhsHTeXBKpwW6XXod0IO2UHr7eTtE2L+3LP6X0u+eT7bAyd0oxp84v3+fn7f7oJ06hauXnTHBaMm4N3cEi11NqUpJJ1t9xG1q9ljFX/wyj087T72xhw64WfvP9w5g2dv+dQg6HheJ8O+9HjmDSJx9TetwCuU4GmFxP6gv6jAA43dk8/YeTAAWDvmuBuUxop5RI1mOxGL/85S955ZVXmDx5Mvfdd19ncTGLxUI4HO72nD3bbDbbAZ83mVRob9/7EPZoNEIymSSRUIjHezcMvb+p2bO+Z+h9Mtn19/9ye7LLdqvVjk6nY9OmTV22v/HG60DHDZl4PMno0ePJycnlmWee4uSTz8Bu7+htiUaj/O53N1BRUcELL7xKVpY6f/MD0VM7JRIKyWQSrzdIKJRQN0ABdLSR3W6mvT3U66klYvBJO6WHvbVT6+IlKIkExqkziYWjnLWgY6RUa2vggM/175Zc4tpyDt2yC2Pe4BRRzRRyPaW2ptdexfv5OsquvRaj00F7e4gjJxdQ7DEzIs/a6+vm7AXl3PLvNpZs9WJ4+D/knX7GAEc+NMn1lDrCLR11r3yBCHFf15HWqd5Odru51x2xqifroVCIK664gg8//JBZs2Zx3333dUnAi4qKaGxs7Pa8hoYGgC6V5A/EvpLwREL9ced7Er9UGwLfk6ysLI48cgHvvfcON998A1OnTmf9+s/56KMPsdu/rP6r1+v52c9+wW9+80suvvh8TjnldKxWG2+88Spbt27hRz+6AofDqd4vcgD21U5q3uwRPUskktImaUDaKT18vZ3aP/sM8+gxfFoRoNXXwvGzhh/0OWZOKuGJpii1K9dSfMzCgz7eUCTXU+pRFIXmt97EMW8+5nGH8MbSXRS7zRS4LYwZ5uxTe5UXOZgxNpcl+sMZ+9KT6HPzsB92+ABGP7TJ9aS+eCwOQG17nP8+uoILvzGOAnfX4nKZ0E6qjjOOxWJcfvnlfPjhhyxYsIAHH3ywW0/5hAkT2LZtW7fe9fXr1wMwadKkQYtX7N///d91nHjiySxd+jF33XU7DQ0N3HPPP8j+WtG7+fMXcOedf2fYsBL+859Huf/+e1EUhRtuuInzz/+eStELIYQ4GMlwmOCG9VinHMorH+9iS1Vbvxx35qRi0GhYsWpXvxxPiFQQqagg0dZG9sxZLNtQz9+eXcOKTQ0HfLwzF4yieJgH3aGzaXzqSZRkeicpQuzLngJz3rDSb581qUjVnvW7776bJUuWsHDhQu6+++4e19U+4YQTePbZZ3nyySe58MILgY6K8c8++yyTJ09m+PCDv2MvenbooTNYsmRFr7cD2O0OfvWr33bb/tRTL3bbNm3adKZNm37QcQohhEgNgfWfo8Tj1BWNYfeqnZy9cHS/HNdhNTLKoWFNrZFver3oHb1bq12IVOZfswqt2Yy2tJz//Gs5h08u5NR5ZQc8sjPPaeaac6YR3JTF7s8+IlJVSVbpiP4NWogUsSdZD8U7rhdLD0UYM4Fqv1VDQwMPP/wwer2euXPn8tprr3XbZ86cOcybN4958+Zx2223UVtbS1lZGU8//TR1dXXceuutKkQuhBBCiJ74V63EWDyM93YGyXeZGT/C1W/HnjujjGX/20X7iuW4jz6m344rhFoCa1ZjnTSZd9fU4g/GuOikCV+sLnNwcx8rTbl8kjuNnPXrJFkXmeuLZD24J1k3SbLer1auXEksFgPgpptu6nGff/7zn+Tl5XHXXXfx17/+lZdffplQKMTYsWN56KGHmDFjxmCGLIQQQoi9UOJxAmvXoD/yWFZsauDbR5Wj7ceK1PNmljHy4xcIrKiTZF2kvVhLC5HKChzHHc8Hq2tYOH0YBZ7eF5Tbl8rGIB85JjD781W4TzypH6IVIvXs6VkPxhRMRh36DFimrSeqJesnnHACmzdv7tW+VquV66+/nuuvv36AoxJCCCHEgQht20oyGCRn2lSumuGgND97/0/qI+P0Wax+6mUKWpoxuD39fnwhBktgzWrQasmeNIXfTjWi7cdE4/BJhTz73lZWNGsYGw6hzUr/5auE+Dol2ZGsTxjuwJXjVDeYAZSZtyCEEEIIMahC27ehNZuxlpUyYYQbm7l7HZqDtd40jCeLjqVmybJ+P7YQg8m/ZjWmUWOIGUxYsgz9er3YzAamlTlZYysnsHFjvx1XiJTyRc/6sFwbcyYWqBzMwJFkXQghhBAHLbxjO4YRI/nbi+vZWds+IOeYMbEYjUbDsuVbUeLxATmHEANNiccJbdlEW/kUrrzrQyrrff1+jgWzR9JidLBu5ZZ+P7YQqWDPMPjPq9r5fEezytEMHEnWhRBCCHFQFEUhvGM7tQVjWbmlsV/nqn+VzWxg0jAbK/TDaF/2yYCcQ4iBFq7YhRKNssWQh1GvoyjH2u/nGDvcybfdLbi2ren3YwuRCpREx9KE733ewPurqlWOZuBIsi6EEEKIgxJraiTh87FNn4PDZmR4vm3AznX8vDE0mVysePsTWUdapKXQ5k1oTCbWNSWYVO4ZkMJYGo2GeTNHom2sJdbU2O/HF0J1ewrMRRIZu2wbSLIuhBBCiIMU3rEdgA1tMHmkB80A9axDR4/h7OEWks2NBNZKr6FIP8Etm4mUH0JVg5+po3IG7DzaslG8ljeHtcs2DNg5hFCLkuiYChUMx7Fm9X+NlFQhyboQQgghDkp4+3Z8BSOoaw0zuXzgkg/o6DH8wXmHMbbQinfJBwN6LiH6m5JIEN62laaC0Rj0WiaNdA/YucyObCqyS1i1tWnAziGEWpRkErRaApF4RvesZ+5vJoQQQohBEdqxHXfpMH44dwKHjHANyjmbSyeya+MuTh+UswnRPyJVlSTDYWbPHM1hpWVkGQfuq7hGo2GsXWFzqx5FUQZ0xIsQg01JJNBotZQX2Sny9H/dh1QhPetCCCGEOGDJaJRIVSWuUWXMGp+P2TQ4/QBryOUDUznx9oGpPC/EQAhu3gQGA4bS0gFN1PeYNDqPZn02ddurBvxcQgyqeAJ0eq741mRmjMtTO5oBI8m6EEIIIQ5YuKICEgkWBd1srmwdtPMeMrYQryGb6o3bB+2cQhys0NYt1JVN4+f3LaPJGxrw802ZPQ6NkmT1Z9sG/FxCDCYlmUDR6QiEYyQVRe1wBowk66KLW265kblzZ3T5N2/eTI49dh4XXXQeTz/9BMkBrr67cuUK5s6dwYsvPtu5be7cGfz2t9f1+VjBYJCWlv5be/G1115m7twZfPLJx/12TCGESGehnTuIG0y8tq6V2ubgoJ13woThaBSF9ZtrB+2cQhwMJZkktGULu91lKIqC25414Oe0ObI5nW2UNEmyLjKLkkjQYnRwxZ0fsqM6c0dYyZx10aMrr/w5DocT6Fg/NxQK8cEH73H33X+hrq6GK6+8elDjueGGmygoKOrTczZt2sh1113Ntddez2GHHT5AkQkhxNAW2b2b5qIxJBUoK7QP2nmtZiNFmgCbGxKcOGhnFeLARat3kwwG2KnYGFfqRDtIc8gPG5+L9/33UZJJNFrppxMZIpkkrO+44WWWAnNiqJk37ygKC7smx6eccjo//OHFPPfc05x33vfIyRnYir9fdfzxff8qtmPHNhobGwYgGiGEEHuEq6qod43DGNZSnDu4RX4Oy1Vo37V7UM8pxIEKbtlMzGBiV2ucc6cPTiFGAEaMZpFxN/rNFZSOLxu88woxgJREnLDOBIA1g5N1ub0mek2n07Fw4bEkEgnWr/9c7XCEEEKoTEkkiFTvpsbgYnhBNnrd4H6tmDcpn+m1n5Hw+Qb1vEIciNCWzTSVTiKRVBhfOnjJuq1sBJ+6DmHtBrmxJTKHEk8QGQLJeub+ZmJA6L74IhaPx7nllhtZteozLrzwEu67725isTg/+9n/8Y1vnITf7+ehh/7B4sWLaG1tIS8vn2984yS+850L0eu/fNm1t3u5//57WbLkA0KhILNnH878+Qu6nXfu3BkcffSx/O53f+zctmLFpzz22CNs3rwBnU7HhAmTuPTSHzN69BgeeugfPPzwPwG45porKSgo5NlnXwagqamRBx74O0uXfoTf72PYsBJOO+1MvvWts7qcs66ulvvuu5vlyz8lmUywYMGxjBxZ3u9/UyGESFfh+nqUaJSJpU7Mw4oH/fxZpSOoM7lJrN3G+COmDfr5hegtRVEIbdnMhCOP4s8L5uBxDPx89T2yHHYKE16218QG7ZxCDLhkkrDOiEGvxaDXqR3NgJFkXfTJ8uXLABg7dhyffPIRLS0t3H//vXznOxcRCPiZPHkqoVCIyy+/jOrq3Zx22rcoLi5m3brPeeihf7B580b+8Ifb0Wg0xGIxrrjih+zatYMzzjiL4uJiFi16hz//+Q/7jeO9997ht7/9FcXFwzpvADzzzJNcccUPeOCBR5g/fyHNzU289NILnHfeBUycOAWA5uYmLrvsQqLRKKeffiYul5vlyz/hr3/9M1VVlVx11TUAeL1t/OhH38fv9/Htb5+Lw+Hgtdde5p133hy4P64QQqSZYEUlAEfOGY3+izong8mQm8d7uTOxrm5k/BGDfnohei1aW0vC58M8ZixWp3nQzz/CkuTzgKyzLjKHkkgwI1nLiT++RO1QBpQk6wepzR/B64922WbJ0pPrNBOLJ6hp6l4Zt7QgG4Da5gDRWNfK6h5HFjazgfZglNb2CAA6nYZEQiHLqCPfbSGZVKhq8Hc7bnGutd+GIPp87ZjNFgCSyQQNDfW88spLLF36EUcdtZBhw0oAiEYj/Oxn/8fJJ5/W+dyHH/4nO3du5777HuKQQyYCcNppZzJ27Hjuuut2PvroQ+bOPZJXX/0f27dv5Te/uZnjjvtG537XXHMlK1Z8utfYkskkd955O8XFw3joof9gsXTEOXfufM4//0yeeeZJrr76WiZOnMxLL73AoYfO7Cww949//I1AwM8jjzzROSf/jDO+zd13/4Wnn36Ck046lVGjRvPf/z5GY2MDf/vbg0yZMhWAU045g8su+x47d+7ol7+xEEKku0BFJW3OQtY3xplkVwatYNYeGq2W4Vlx1vgzd9kekRlCWzYR1pv440c+LnR4GTXMMajnLy/MZkmFiRZvCLdj8G8WCNHflEQCvU5LtsWodigDSpL1g/T+qmpe+mhXl22HTcjnspMn0OKL8LtHlnd7zr9+ubDjv69uZHtN16UGLj3pEOZMLGD5xgYef3tLl8cmlLm5+uypRGKJHo9755VzsffTC/bii7/TbZtOp+OEE77Jz3/+iy7bDz10Rpef33//XYYPL6WoaBhtbW2d2484Yh533/2XzmT944+XYLNlc8wxx3fuo9frOfPMs/eZrG/evJHm5ibOPvvKzkQdYNiwEv75z0fJyyvo8XnJZJIPPniPiROnYDZbusQ2f/5Cnn76CT7++ENGjRrNxx9/SHn5qM5EHcBsNnPKKWdw11237zU2IYQYSoK7drExbwIrX9vInVfMVSWGIo+FD5sNBMNxLBk8b1Gkt9CWLdQNn0RNSwinbfCTi7Fjijh81XskWsaAY9ign1+IfpdMsEQ3nBWLt/Ot+Zk7TVU+1Q7SUdOKmTY6t8u2PV8W3NkmfnvhzL0+9+Jvju+xZx1g5vg8RhV33HX9as86gMmg6/G4FlP/NedvfnMzLpcbAK1Wi9Vqo7R0BGZz97uxbreny8+7d1cRiUQ46aRjejx2fX3Hmrg1NTUUFRWh/doyIqWl+65UWlvb8fySkuHdHhszZtxen+f1tuH3+1m27OO9xlZXV/vFOWqYNWtOt8dHjBixz9iEEGIoCVRUUus5krJCO5pB7lXfo7jAAc1Q3dDO6OFuVWIQYl8URSG4ZRNV5ceSQxY5KgyDzx0zkiNb/kpW4xwok2RdpD8lnmC31klWD6ONM4kk6wfJaTPhtJl6fMyg13UOee9JoWfvS9zYLcbOXnK9Xks8/mVSr9Vq9nnc/jBp0pRuS7ftzdeT7WQyySGHTOTSS3/U4/7Z2R3r8Go0EIlEuj2uKMlu27oeP/HF8/v2xTCR6Dju3LlH8q1vnd3jPjk5uZ3H7im2ZFKGWgohBEAyEiFUW8duu4njBnF99a8rLs0n/7NNhFvbQJJ1kYJiTY0k2trYkbAxfuQgLtn2FbrsbNpzSqhYV8uxs1QJQYh+pSQThDV63Bk+oiqzfzuhioKCQny+dmbOnN1leyQSYcmSxeTn5wNQVFTMZ58tJxqNYjR+OSSsunrfS4sUFBQCHT34X3f//fdiNBq5+OLLuj3mdDrJysoiGo12i621tZU1a1Z29tYXFRVTVVXR7Rj7i00IIYaKSHU1rXobwTiMLFIvWbcVFXDR7r9QrB2rWgxC7Et4x3YCuixq/QlOHsQl276uIn8sbzTYOCqeyOjq2WKISCQIoceSZVA7kgEl66yLfjdv3nyqqip59923u2x/9tkn+e1vf8WKFR3z7efPX0g4HObZZ5/q3EdRFJ599ul9Hn/cuEPweDy89trLRCLhzu21tTU8/fQTNDU1Al/2+O/pqdfr9cyZM5cVKz5l3bqu68Q/9NA/uP76a9m5czsARx65gJqaat57753OfWKxGP/733N9+lsIIUSmCu+uIqo1MHaYnREq9qzr3R7Q62mrrlctBiH2Jbx9O/YcB9d/dwaTyj37f8IAGTXMSQItu+p8qsUgRH9REgnC6DN6jXWQnnUxAL7znYtYvPg9brrpelatWsHo0WPZvHkjr7zyP8aNO4QTTzwZgBNO+CavvfYy9913N1VVlYwePYYPP3yfrVu37PP4er2eK6+8hhtv/BWXXXYh3/jGSSSTSZ577mksFgvf+973AXA6O+5ev/TSC/h8fo477gR++MPLWbVqBVdd9SNOP/3bDBtWwsqVy3n33bc5/PC5zJ7dUTX+3HMv4N133+Kmm25g3brPKSws5M03X6e5uXnA/m5CCJFOwrt2UZaTxcILZ3WZqjXYNFotHxUfxtrPFf56gmphCLFXoR3bsYwsp1DFESgApaNL0G5rYOf2WkYPc6oaixAHS0kmOdpYz8RRh6sdyoCSnnXR7+x2O/ff/zAnn3w6H330IXfeeRvLl3/Kt799DnfccQ9ZWR1F9LRaLbfffjdnn30+S5cu4W9/uxOtVseNN96y33McffSx3H773VgsVv75z/v4738fY/z4Q7j//n+Rn99RDX7GjFksXHgsn37asY56JBKhuHgYDzzwKPPnL+CNN17lrrtuZ8uWzXz/+z/g5ptv7eyNt1gs/P3vD3H88Sfy5puv8o9//J2ioiJ+9rP/G7g/nBBCpJHg1q2ERk0kkVQvUd8jL9uAN6EnFImrHYoQXSRjUSJVlTybLGft9iZVY7GVlZIT9bKrQt04hOgPSjzBdFM7ZSqO7BoMGkVRhmzFrEQiSUtLYK+Px2JRmptr8XgKMRjUW8Pv6wXmRGr6ejulyutHfEmv1+JyWWltDcg1lcKknVJfIhhk80+v4M5R53POcWNYOE3d6tKr//Mcd+928esLplNePLjrV6c6uZ7UFdq+jfV/voO/l53Jj0+byIxxeT3uN1jt9Mj1f8M0ooxzLzlxwM6RyeR6Sh07br+dDYZCDr/oW7jtWV0eS/V2crut6HS96zOXnnUhhBBC9El4xzYajU7iCpQXqZ8cFxd7QFGorveqHYoQXYS3b6cmu6Mw7uhh6l8rx7iDHBneqnYYQhy0YFLL86FCqpv23vGaCSRZF0IIIUSfhLZupc5RjF6noSTfpnY4WIsLccZ81FS3qB2KEF2EdmynLmckuc4sHHtZ6ncwGYeV0FDdSDAcUzsUIQ5K7IvllA297KFOV5n92wkhhBCi34W2baXOM4KRxQ6MKbAElDE/nwt3v8Y3i2XOukgt4R3b2W3KYVSKTM/QFJVwj30Byz+vVjsUIQ5K/ItkXa/P7HQ2s387IYQQQvQrJR4nvHMHwSw7h5SptwzVV+nsDswGLbGGBrVDEaJTvK2NeEszJ463s/BQdes67GEfMRxPzMvOnbLUoUhNyUiEZDi03/3iCelZF0IIIYToIlxZgRKNcs3J5Vx40gS1wwFAo9FQVzCav6zTSkV4kTLCu3YCMH3WmJQpfGgsKCQ/2kZlg1/tUIToUf1/HmX3X//C/mqg6xJxSo1RzBm+zrok60IIIYTotdDWLSSNJrKGl6LTatQOp5PZ7aQ2bqSmObOLDYn0EamsYLtnNEsrw2qH0kmj11OclaA6oJBMDtkFoUQKizc1Ed6+jdDmTfvcLy/h40fFreQ5zYMUmTokWRdCCCFEr4V3bOf94fO59Yk1aofSRVGhCxSFmkZJ1kVqCFdW8LlnPMs2ptb0jOE5ZvTJBG3+iNqhCNFNwucDoOX1V/e9XyKBolW/ZspAk2S9V+TOo+i7/Q3fEUKIdBTZXcVuowePI2v/Ow8iW3ERzrif6ro2tUMRAoBwRQVVGntKLNn2VePKcvlp1XO4bEa1QxGim7jfh6mkhOD6dYQrK/a632ath+u25+APZfbKBpKs74NOpwM0RCKpM3xJpI9oNAxovngdCSFE+ktGIvgbW6iNGhhT4lQ7nC6MhYXkRNvYXdumdihCEPe10+iPEUhoU6YS/B7m4SUQjRKtq1U7FCG6UJJJkoEAjvkL2F0wjtdeXLrXfRNKxzQsvS51pmMNhMyekX+QtFodZrMVv7+NeDxGVpYFrVaHRjO4L4pkUkMiIb20qS6Z1BCPJ0kmE4TDQcLhAGazDe0QGKIjhBgaIrurqDHloACjUy1ZLyjk8NbP8eSVqh2KEEQqK6k15QBQVmRXOZquTCXDeTdnBv5XtvF/lxWrHY4QnRJ+PygKeoeDJTnT2BU0cMZe9o0lO/IxQ4Yv3ZZSyfoDDzzAo48+ykcffdTtsTvuuIN//OMfPT5v+fLl2O0D80Zot7sxGEz4/W2Ew+rMg9NqtSSTSVXOLXrvq+2k1eqw2z2YzVaVoxJCiP4T2V1FjTkXW5aeIo9F7XC60BqNlGZrsPnr1A5FCCIVu3DqYpwwqwRrlkHtcLrQ2WxYjFrWtcVQFGXQO6GE2JuEv2O+us5m59CcJiqrkiQVBW0Pr9E4oEFBp5VkfVAsXryYu+++G4ej56FCW7ZsoaSkhCuuuKLbY2bzwFUB1Gg0WCw2zGYryWRHr+lg0uk0OBwWvN6g9K6nsC/bKYSiaNBqtfLhJ4TIOJGqKo7KauaUi2al5HtcomAYr1YkOb45QKFHbpYK9YQrKxmVb2XBwtFqh9KjEqeRxVEtzd4wORleTVukj4TPhwK8vCWIyWAiqUniaw/hcHS/OZxQNOhT72Oo36merCuKwuOPP86tt95KLLb3AgFbtmxhypQpnHrqqYMY3Zc0mo65x4M9/1iv15KVlUUolCAel971VCXtJIQYCiJVlWSVlKRccbk9zAUFfLAzl/JqryTrQlXBykoqymfiDsdSrmcdoHSYC3ZARb1fknWRMhI+H1usJbzxeTPfGmcGQrQ0eXtM1ie1b2PmkZMHP8hBpvq4gbPPPpubb76Z2bNnM2HChB738fv91NTUUF5ePsjRCSGEEAI6Cv+01jTwQGgUO2vb1Q6nR7biIhwxHzX1qRmfGBoSwSC1bREeb3RRnaJLCeaWFmONh6jY3ax2KEJ0Svh8rLeXM7LIzqxRLo5oWUNWouclBo2JKDnmzK8LpXqyXlNTw0033cSDDz6I1drzXfBt27ahKEpnsh4KhWQOtxBCCDGIYk1NNGCmIqjFZEjNL0jGoqKOivA1rWqHIoawyO4qarNy0GigND9b7XB6lDV8OOdWv8WCvLjaoQjRKeH34TfaKPJYsbvtzGtZg1MT7bafoihsMJfwYmXmj4NXPVlftGgRZ5999j7nvm3ZsgWADz/8kKOOOoqpU6cyffp0brzxRkKh0GCFKoQQQgxZkapKGoxuDDoN+e7UHDZrLCgkJ+qlpkW+Gwj1RCorqDPnUOyxYjKm5o0tQ34BuZoQ1FarHYoQnRI+H0GdGbvViM5moyorj8pabw87JqgzedjUlvn1vFSfs240Gve7z55k/fPPP+fyyy/HZrOxePFinnjiCbZv386jjz6K9gArAepTvNy/Tqft8l+RmqSd0oO0U3qQdkpNsZrdNNryKM6zYTLqU7Kd9HYbo7XtWG0hdDpNShbBG2yp2E6ZLlZdRZ2tgDHFjl5/zxz8dtLiLR7NC+ui/OTIGA6baZDOm97kehpYyYCf2fo4U0YdgcmRzTs5MynfEWDCwq5/72QS4hodep2mx2ssk9pJ9WS9N+bNm0d2djaXXnopFktHgYETTjgBl8vFQw89xNtvv83xxx/f5+NqtRpcrvQoQGO3p2YvhuhK2ik9SDulB2mn1FJXV02ztZTxw1xdPjtTrZ0mFFmYrKnE7bapHUpKSbV2ymS7aqpxWwqYfkhBn79nDmY7uUqHsWm3leZAjBEl7kE7byaQ62lgVIcCHJOrYdy0EgCyieCLJLpdR/GghoRGh8mg2+c1lgntlBbJ+vz585k/f3637eeddx4PPfQQn3zyyQEl68mkQnt7sD9CHDA6nRa73Ux7e4hEQubppyppp/Qg7ZQepJ1Sk7+qmm+OzKd4cgGtrYGUbSddXgGb1lfA9kYK3Km1FrwaUrWdMpWSSBCqqOLSsw/HU+6mtbV3BebUaKec4jxMlVHWb6lnZL7c3OoNuZ4GVlurn3prCfY6L2aTHrsmQX0w0e06Svj9xDVatCg9XmOp3k52u7nXvf5pkazvjcfjASAYPPCEO12W2UokkmkT61Am7ZQepJ3Sg7RT6lCSSWINDYw90oEr19alXVKtnfT5BTy3xcZhn1Zy9jFj1A4nZaRaO2WqSHUNgaQWJb/4gP7eg9lOhuJheKKrqapqIh4vG5RzZgq5ngZGXQgebcxhZEuQYbk2svVJtsa652vxSIyxgUoswybtsx0yoZ3SYiD/hRdeyMUXX9xt+44dOwAoKSkZ7JCEEEKIISPe1kaV3sV77TaSSmoX9DEWFZMTbaO6VirCi8EX2V3JeznTueuT1F8+0DSsBHfUS02jX+1QhEBRFHyRjsTabu2oaZZrSmInivK1zx0lkWBMoIrZpZk/eiotknWn08nHH3/MqlWrOrclk0nuvfdedDodJ554oorRCSGEEJkt1tTINuswPtodQ5viRduMhYXkRNuoaU7taW4iM0Wqqmiw5DK8wK52KPuls1g4VGlgfrZP7VCEIBkKEdAY0QC2LAMA0xwxLuPz7sVCkwmqs3LY7UvvXvPeSIth8Ndccw0fffQRl156KRdccAFut5s333yT5cuXc9VVVzFy5Ei1QxRCCCEyVqyhgUaji2Epumb0V+mz7eRpIiwLK4SjcbKMafFVR2SIQGUVTfopHJMG1wpAuVuPLrhb7TCEIOH3E9CZsZm0aLUdybnOaiO8ezdJRelyo1hJJHjfcygFO0KMnaNWxIMjLXrWhw0bxn//+19mz57NY489xm233UYwGORPf/oTP/rRj9QOTwghhMhoscYGWrKcFOakRxGqYqeBfF2E9kBU7VDEELO73ksCLaUF6ZGsa3LzWNqkpba5d4XwhBgoCV87BiVOqSerc1s4K5s/Zs1nzdamLvsqiQQJjQ59BizNtj8pdbv5scce2+tjo0eP5m9/+9sgRiOEEEIIgHBDI17dKPJc6bEMTmmRkx/u/Jg81zfUDkUMIfH2dlrCGnQaKMlNjxtbprwC3qg14NjZQqEnPZYzFpkp4fMxu20DI0+5rHOb1W4hgYZWf+RrOyeJa3QYhkCynvm/oRBCCCEOSqipidlmHyOLUn8eLoCxqIhIbS2RSEztUMQQEqmqZFyggju/Mw6TUad2OL2SlZ+HK9pOTV2b2qGIIS7h96EAOtuXN7oM2TZs8RBt3q41SDp61rXo9Zmfymb+byiEEEKIg6JprOOMch0j0qBoFoCxsIgXPYfzt2dW7X9nIfpJZHcVGqMRS2GB2qH0mjEvH0/MS0196levF5kt4fPzYOlp/G9pZec2ndWGLRGktfXryXoceyyA02oY7DAHnSTrQgghhNirRDBISxgazDlqh9JrxsIiHHG/VIQXgypcU8v9Jafy2Zam/e+cIgx5eXiiXuq8kf3vLMQASvjb8evNXUal6GwdPeutvlDXnZNJzq59l+Mmpc/n0oGSZF0IIYQQexVramSVYwwPb4irHUqv6V0ucpUALaEk4Wj6xC3SW219G60aM9aslCoJtU9ak4nhuiAjs6Ikkpm/DJZIXeF2PxGNAbvF2LlNZ7VxbNOnfGdy1xoQSiLxxQ7pc60dKEnWhRBCCLFXsYYGWg3Z5LktaofSaxqNhgKHCYD6ltB+9hbi4CmKQlVrx+oDw9OkEvwe411avq3fiU4raYFQT3t7x3u1w/plsq61WrHHg1hj3ees3zPiTD7Y3DqoMapBrkohhBBC7FWssZE2o4M8T3pUt94jL98BQGObJOti4CV8Puqx4MrSYM1Kr3m0hrx8mhraaPt6xW0hBlF7sKMgqP2rybrZTJ05l4dWBYjEEp3blUSCoC4LtJpux8k0kqwLIYQQYq+ie3rWXenTsw7gKirkJ7WvMG105s9pFOqL1tXSZHRS7Emv6wTAmJfHv5jIW8ur1A5FDGE5/gZ+VtJC4VeuIY1GQ8JsY20rNHnDnduT8TiKRoteL8PghRBCCDGE+RubsekS5LvTY431PUxFhWQHWkh629QORQwB0doajm1eztnHjlM7lD4z5OXjjnqpb/SpHYoYwjQBH8VuMwZ912UP3Vkd/236yiipWLyjl91gSI8lEg+GJOtCCCGE2Ctdcx2/LGtj2uhctUPpE0NuHssd43ly0Ta1QxFDQLS2llyXleICh9qh9Jkhr2Ot9bomv9qhiCFKSSbZoM3hlcbuN4WdFgM6lC4969EvhsQbDdKzLoQQQoghSlEUYl4veqdL7VD6zODx4NNb+Lw6oHYoYgioqm7hNdcM/KGY2qH0mTEvH3esnUZfjGRSUTscMQQlg0F2Z+Wy0ds9NdXbrDg0UZq8X/asZ2kSXFj1CmNLnYMYpTokWRdCCCFEj5LhMEus4/jrZuP+d04x2qwsXNooLaGkJCBiwO1qi7My7sJkSL+v1tqsLHIMCfQahfZgVO1wxBCUCPgJ6MzYzd17ynVWG/MSlUwe6encpk0qFERayLal1/SsA9Hrd5TTTz+dP/7xj7zzzjt4vd6BjEkIIYQQKSDh9dJqyMZoTM+hhjlmLUk0tPjC+99ZiAOUjESoj+nJMWu6zbdNF2OdGq53bsVpM6kdihiCEoEAAV1Wl0rwe+hsViYHdjB+hLtzmzcU563c2TS1Z/4KBr3+9D322GNZs2YNL774Ij6fj9GjRzNz5kxmzZrFzJkzcbnSb4icEEIIIfYu3u6lzZBNSZpVgt8j15kFQWhsC5PjyPweGKGOaH0djUYXxe70vE4ATPn5RGuq1Q5DDFHJQICgLgu7LavbYzqrjeZgkprPazliUiEAvkiSlY6xfDMUI9eZ2e/tvU7Wf/zjH3f+/86dO1mzZg2rV6/m73//O9u2baOsrKwzeT/hhBMGJFghhBBCDJ5EWxttBhuH5mSrHcoByfFkc2LbRvJdh6sdishg0doamowOJhWnb8eVIS+PRyqMlL+/jW8fNUrtcMQQkwj4mdq+lUlj53Z7TO/xsFux8tKrG5k2OhdLlp54PAHo0evSb9pJXx3QuLaysjLKyso47bTTiEajrFixgueee44XXniBJ554QpJ1IYQQIgOE2rwE9BZyPTa1QzkgWTkepnz8Ia5sGdorBk64ppY54d1MG3+k2qEcMGNePsl4C1U1MtVVDL6EP8DM4HZGj8nv9pixsAhHvGOlgiZviOFZ2UQTSQAMeknWu4lGo3z22WcsW7aMZcuWsX79erKzs5k+fTrXXHMNs2bNGog4hRBCCDHIlHYvP2xfxyGj56kdygExeDzs1LpoW1vFzCnD1Q5HZKhEQx3z7AFKCu1qh3LADHl5uGPL2NEsqyeIwRf2+dnmGklhKIbNbOjymLGgEEe843XZ7A0zPD+bRLwjWdfrNIMe62DrdbJ+7733smzZMtasWYPNZmPGjBmcdNJJ3HTTTYwePXogYxRCiH6VjMXwffYZ2TPk5qIQ+6K0eymw6bt9eUoXencOG7LL8C6TZF0MnF2NISI5IyhRO5CDYPhi+bblgTjxRHJIDC8WqaOlPcwz9tmMbPQzdnjX6SRaoxGnKxu9RqHxi7XW7boEM4PbsZjSdzRLb/UpWc/Pz+fqq6/mrLPOwmzO7Mn8QojMFdi4gdr7/07Wn0dhcLv3/wQhhqgNrQqbTeP5oaKg0aRfD4bB48EZ87HdJ8tRiYHzWcROTTiXo9QO5CDozGZy9AkUoLEtRKHHqnZIYghpD3S8R9ssPS8TaioqYkyoDbOxY7WFPH2M40MbsWSl50olfdHr22a333478+fP57///S+zZs3i3HPP5c4772Tp0qVEIplfNl8IkTmUSMeHQjIUVDkSIVLbrpCerUl7WibqALrsbFzJEIGYQjAcVzsckYESfj9NGgsF9vQcffJVJS49lzl347F3r8gtxEDyhWIAZFt6vo6MhUWc0fwx86YUdewfVag2ekgqyqDFqJZe34446aSTOOmkkwCoq6vrnLN+/fXX09DQwOTJk5k1axazZs1izpw5AxawEEIcLCXW8aGQDMvay0LsS1tciyt9p+Gi0WrxWDp6YhrbQpQWpGdVe5G6og0NtBgdHJKb/q+t7PxcTHUVGA3puVa8SF+BSAKNVsGWtfdkPdbcTHubD7szmw0+Hc9lz+GIQY5TDQc0IaWgoIBTTz2VP/zhD7z77rs88cQTjBw5kkceeYSLL764v2MUQoh+lYx/kayHQipHIkTqUpJJ2hQTHkt6DzPMc5gYp/eRpoMDRIrz1dbj11soLvaoHcpBM+Tls9Rn5f1Vst66GFyaSJhiUxyttuc3alNREZ86D+GXD65AURTiCdAqSbRD4I29z5/AoVCI9evXs3bt2s5/DQ0NjB8/nnPOOYcZM2YMRJxCCNFvvuxZl2RdiL1J+NrxGqxMcKT3kFh7jpOzd69meP6paociMpCvvpERkSAlxTPVDuWgGfLyqNA2UbG+lqOmFasdjhhCJrZt4cjZ5Xt93FhYiCMWIBxXCEbixJIKepKDGKF6ep2s//rXv2bt2rXs2LEDvV7P5MmTmTlzJmeddRbTpk2TgnNCiLShxDvmrsoweCH2Lu71MrN1AxNHnKx2KAfF4PbQvHYD3kAUh7Xn4kVCHChrWz0XGuoYnp/+w+CNeQU445+ytVXquYjBoySTJIJBtDbbXvfRZpnJ/uL9uz0QJZFQ0JP589WhD8l6U1MTJ598MjNmzGDSpEkYDOlfSEMIMTR19qzLMHgh9irR7mWWdyNl5ZeoHcpB0Xs8PJU9g+K3N/OD0yapHY7IMK0NLVhz89UOo18Y8nJxxny0yPJtYhAlg0GeLViAZ5eWH+5jJTZnjgOi4A/F0JLAxtBY5aPXyfo//vGPgYxDCCEGTVIKzAmxX40NbWyxllBq3ntvRzoweHKwxzfT0hpQOxSRgV4NFxIOFXC92oH0A53FivuL5dtafBHynDJqVgy8RCCAX2emwLjvkU/OghyoBF8wxtysVg7zNwHpPfKrN3qdrI8fP77XB924ceMBBSOEEIMhGYvTrrfgkjnrQuzVppoAzxcu4HhDeheY07s9ZMeDbPfJMrOifyXDYZo0FsodJrVD6TeFTiPzza0YpFddDJJEwE9Il4Xdtu/6KM7iAq744CkmlR5B4/sJ0A2NVQt6/Qns8Xhobm5mypQpHHfccUyYMCFt110VQgxtK1t1PDniTK721ZCrdjBCpKhmXxRbEgz69P5CpHc6yY4HaQslUBRFvruIfhNpaKDFYGduXvrPV9/DnetkYesWXNmZcwNCpLZkIEBQZyI7e98jOYweD9Z4CMXv562Am4ZkPv83SDGqqdfJ+pIlS1i9ejXvvPMOTz/9NJFIhKOPPppjjjmGWbNmodXKHTghRHpojHR8WW8KDo1KokIciJZQEpcupnYYB01rNOLQJUgqCoFwHJtZau6I/tFQWUdcq6doWI7aofQbgyeH7TsaSNb5KC3InJsQInUF233EtXocTus+99NZrbydM5ORK6vxJXT4GRrv5X3KsKdOnco111zDG2+8wYMPPkhubi5/+ctfOOKII7juuut49913iURkmJkQIrVNMrYDEI7GVY5EiNTVGtXgMmRGtd3x5jA3D6uRRF30q4a6ZgCKMmCN9T30OTm8ayjn1Y93qh2KGCK0oSCXVr/CpDF5+97PaqPe5GZXvZ+YokGvyYzPp/054Ilo5eXllJeX84Mf/ID6+npefPFFfvGLX5BMJlm1alV/xiiEEP0qVwkxLNSAHikwJ8Te2GIBhrnTu7jcHkanA8XbpnYYIsMMD9bzq2gFea4FaofSbwyeHByxzTS0SEFGMTiUgJ88UxKbed8F5nRWK5ZEGF8ohlYB/RAZ1H1QVWOqqqp49913WbRoEStXrqSsrIyjjz66v2ITQogBURnWMypQxSitT+1QhEhJiqJwSsMS3LNOUTuUfqFzuHig3s1x6+uYM6FA7XBEhog1NWLJ9aDNoDoIBk8OzriPbV65mS0GR2VLlCXOGVwaiWM27T011ZrNWBIRmsIJzNKzvnerV69m0aJFvPvuu+zatYtDDz2Uo48+mltuuYWSkpKBiFEIIfrVjmgWn7gmMsu/WO1QhEhJbdW1RMJRjPmZkdga3E7aag3UNElvoeg/zwUKyLE4OF/tQPqR3uPGGfMTjCkEwzEsWTJ1RBw4JZkk0e5F73TtdZ9af4LV+iL0+1mBQKPVYtEl8UeTnKRUorM6+zna1NTrZP3Xv/41ixcvJhgMMnfuXC677DLmz5+P0+kcwPCEEKL/xRIKYZ2Jp7Om8hu1gxEiBb3+3mY+HX4qt48erXYo/ULvcJEda6C1XXoLRf9QFIUqsnEYM2stcq3BSI5JodCUIBCOS7IuDopv+TIaHnuU8rv/jmYvxch9kQQmEhh6Ma59vKaVYS4/BXU+jFlDowBir5P15557Dr1ez4QJE2htbeW5557jueee63Hff//73wcUzAMPPMCjjz7KRx991O2xcDjMvffey6uvvkpLSwvjxo3jqquuYs6cOQd0LiHE0BX7ogi8TyNL0wjRk831QUp0IfTWzJizrne5yI7tpKUtqHYoIkPE/X68eiu5LovaofS74Q49VzgqyHUeq3YoIs211zWyReuhLBxCZ+m52nsgqmA19G51nhJzAqO2hSVKHrlRK5kx9mvfep2sX3755QMZB4sXL+buu+/G4XD0+PjVV1/Ne++9x3nnncfIkSN59tlnueSSS3j00UeZMWPGgMYmhMgs0SSgBb/OTDIWQ2uQngMh9giGY+yOmTg9P3NWS9A7XWTHg+ySnnXRT5qrG0hodOTm9vy9NZ0ZcnKINjURTyT3OzRZiH15pkLH50VHc5TPj2VvyXocrL0coBIyO1jdamCJtoAJ0Tjz+zHWVNXrZL2srIzDDjsMj6d/l6dQFIXHH3+cW2+9lVis5/Vcly5dyjvvvMN1113HhRdeCMBpp53GKaecwh/+8Aeef/75fo1JCJHZChNeHIoTr85CqD2A1eNUOyQhUsaGtTtRNFomTCxVO5R+o3c6meTbzoyxs9UORWSIhtoWAPKLMmfZtj30nhzua8hl9DtbueD4sWqHI9KYPh4FzCih0F73GRnYzeiRub06nj/LwYveYtCAQevvpyhTW69vl3344Yd861vf4uSTT+YPf/gD7733Hn7/wf+Rzj77bG6++WZmz57NhAkTetzn5ZdfxmAwcNZZZ3Vus1gsnHnmmaxfv55du3YddBxCiKFjaqSK05JbAGhtHRpv9kL0VvW2SuwxP8OnjFM7lH6js9vJjbczSi/Xu+gf7qiXMxo+oKA4R+1Q+p3B48Ec8dPYKtNGxMFRYjGKQw1oIj2PalIUhTFt25lTvO9l2/awW7/cT6/LnFUY9qXXyfqtt97K+++/z913382IESN44YUXOO644zjnnHO48847WbZsGdFotM8B1NTUcNNNN/Hggw9itfY8PGLdunWUlZVhsXSdF7QnuV+3bl2fzyuEGLra41pyrDqu3PEUOcaE2uEIkVJmtm3kp4b16LKy1A6l32i0WqLOXN7cFqJFhsKLfmDytTDBFMBkyrxpVIacHJwxSdbFwWuO66k251Hd0PNSuUo8xi5jHq30roZQdvaX4+UN2qGRrPd56baysjLKyso477zzUBSFDRs2sHTpUh544AHWrVvHIYccwsMPP9zr4y1atAijcd93U+rr65k8eXK37Xl5eUBHwi+EEL31knkSZiycnFyFEomoHY4QKSOZSBDcvAnnwmPUDqX/OVy82WRmXIMftz1zbkQIdXxWGyXpHE2Z2oEMAIMnh+xYgFZ/FEVR0GTQOvJicC3wruXf7iOpaAzR09oiyXCYFwqO5PhWba+uJVO2DWMyRlRroNDcu6J06a7Xyfobb7zBwoULuyTWGo2GCRMmMGHCBC655BJisRirV6/uUwD7S9QBAoEAZnP3ygNZX9z1D+1jHsT+6HuxTICadF8U9tBJgY+UJu2UHva0TwwtTqOel/OOYMYOL0ePl3ZLJXI9qWfduh3ck/dNrisZtt/Px3RrJ5fLirZdodUfSfnP/v6Ubu2ULpYHrGSbsjmln15LqdRO2rxc7PEA0YRCOJYg29K7IcpDQSq1U6pTkkmKWiswOaL4Qske33fj0TARrZFsm6lX78sGu42y4EZGxxqZNGn8Xp+TSe3Up3XWNRoNxxxzDCeffDJz5sxB+7X18gwGAzNnzuz3IPfnQO/4abUaXK6eh96nGrs9s9bxzFTSTukhjhaLxURFlodqbyxt3geGGrmeBl9jfRthrZHRU8dg6+V1kS7t1FKQi8sboj0UH5LXfLq0U7poTRgY5dT3+2spNdrJyli9n99PjjCs0Il2iAw37ovUaKfUFmxpZbFrChGdkVAs0eO1Ul+XBI0Gjye7V9eSku/htLoPaDHY0ZoP3e9zMqGdep2sL126lMWLF/PGG29wxRVXYLFYOOGEEzjllFN6HKLenywWC+Fw9zlme7bZbAe2DmwyqdDentrzcXQ6LXa7mfb2EInE0BjukY6kndKDTqclOzuLmEaHVqclOx6iyRumtTWgdmjiK+R6Us+Oimac8SBRg3m/10W6tVPSko0z4qWi1jukrvl0a6d0EI/F8GqzcNl0/fZaSrV2Mnuc6Btr8XpT+3vyYEu1dkpl1VuqWeruyBFb/dEer5W66o5VFXT63l1LIUVPXKPjn6WncW5DHM9enpPq7WS3m3vd69/rZN1oNHLsscdy7LHHEolEeP/993njjTf43ve+R05ODt/85jc5+eSTKS8vP+DA96aoqIjGxsZu2xsaGgDIz88/4GPH46nXgD1JJJJpE+tQJu2U+pR4nDhaDHod2URoC0ubpSq5ngZfrTdKrjZCIqEASq+eky7tpMl2MKp9Dda82WkRb39Ll3ZKB7UV9SgaLTme7H7/m6ZKO+ndOTxZZ+PwdXXMGJendjgpJ1XaKZU11rcBMDFUQVG2tce/VzgQwhVtx2Yz9+7vmWXhlfy5AOi0mv0+JxPa6YAG8ptMJo4//nj++te/snTpUq6++mqqqqo477zzOP300/s7RiZMmMC2bdu69a6vX78egEmTJvX7OYUQmSkZi/ODyhc5aZSJbGJ4+76IhRAZqyGiJd+SmUNe9S4X09q3cNzYbLVDEWku0eZlcvtWiodl3rJtexg8bioSFnbUtqsdikhTbS0dS2WeaKpjtrahx31ydFF+UPkipcM8vTqmzmrFmIwBYMyA+ei9cdC/pcFgwG6343A4sFqttLa29kdcXZxwwglEo1GefPLJzm3BYJBnn32WyZMnM3z48H4/pxAiMymxKBpAZzRwiKaF45w9LycixFB0fssHHDlMp3YYA0LvdJFAy66d9USismSjOHD2qJcTG5aSV3LgIztTnd7lJjvqk6UOxQFrbQ+hUxIYnE5qAz33bidDIdBo0PSi4DiA1mLpTNYzoXhcb/R56TaAaDTKkiVLePvtt1m0aBFarZbjjz+eP//5z8yYMaO/Y2TevHnMmzeP2267jdraWsrKynj66aepq6vj1ltv7ffzCSEyVzIW5z/FJ3BCU5JhphhGXf/fYBQiHSXDYbJbaskdlpkJiN7ppM1g45+LGvlFfjvjSl1qhyTSVF1NK15bDroeVirKFHqXG3u0gmZZa10cIFcywKGRJj7X5PBqzMUDPSwD+GlNlP+NOIt7FOhNvXCNTodJ2zFFSyvJelehUIj333+ft956i8WLFwNw9NFH8+c//5kjjjgCvf6A8v5eu+uuu/jrX//Kyy+/TCgUYuzYsTz00EMDcnNACJG54pEIu815hJJaokYrq7wmjvRFcGWb1A5NCFWtXr2T9/MO5we5BWqHMiC0ZjMug4IGhYa2kCTr4oC9UZmgOfdwBn/9o8FjcLuxxwPsbj/w5ZHF0DZaaaVEV8W2rCkkNFpCkTiWLEOXffzhOAmtsU8rDpRoO24gFWVn5iiwr+t1hj179mygo5f7lltuYcGCBZ3rnPeXxx57bK+PWa1Wrr/+eq6//vp+PacQYmiJhCMAGI16NOYs/ucrJL+ylcMmZGaCIkRvbdnZxC5LEVmFmXktaDQaLAW5OLVx6qW3UByElrCC25jeRav2R+9yMypQxfCyw9QORaSp2tYIWTY32ZaOIe7twVi3ZD0YSZClxPt03HJTmCt3PEX2ggv7K9SU1uvxA7/5zW+46aabAHjrrbd49913BywoIYQYKOFgR0U5g1GP1WzCqYSpqJd560LUtATJSfjRWTN3DXJjfiGuRICGVuktFAeuJaHHY8nsXj2d3U5RvI1ZtqGzzKHoX08Gi/lAU4LD1jFy0euPdNsnGE2SpelbDZEsqxlLMoJugEd1p4peJ+uBQIDrr7+eSCRCKBTi2muv5Y477hjI2IQQot9Fwh3JusloQJuVRWGynYo6SdaFqA8myTNlduE1Q34+rmAzsTRfykeoJxpL4NeYyLH37+jSVKPRalGcbpbt8kuROXFAfEk9DouuY03xZIJQoPvrKBRXMGv79n6854ayRpvZN8z26HWy/tRTT/H73/+eBx98kPvvv5877riDxx9/HEXp3TqsQgiRCszaJCfVfciwXCtas5n8aAsV9X55LxNDWjyRpDluoCDbsP+d05ixoIBjaj/iim+Uqx2KSFPetgDWeJC8nMxfAlDryuGp+my27G5TOxSRZmLxJCGtEafVhC3bwjU7HmdiYfeCjEdTyWm2npd12xut1dbxPzpJ1ruorKxkzpw5nT8vXLiQUChEQ0Pf/sBCCKEmEwkm+nfidFjQZpkpCdQxudxDWJZyEkNYIpHkGO9qxhVm7hB4AGN+ARogWl+ndigiTTkSAa7Y9SzjRmbuGut72Nx2sojT2t59+LIQ+9La1jF9wukwo7da0QCJYPdaIfZIO/nWvlV17+xZl2S9q3g83qXiu16vx2QyEY1GByQwIYQYCM3tYT5zjCUUB21WFsW+ai47+RDMpqEx90mInujCQQ5tWkdxaWYu27aHIS+fNr2VX/2vki1VbWqHI9JQrLkZAIPbo3IkA0/vcmNPBGmWYfCij3wtXkyJKG6XDa3Fwkv58/jf8ppu+72TKGZjwtmnY0uyLoQQGay6JcLbubMJJkBrzoJEgra2AHUtUh1aDF07NlexyTocY35mJ+takwlHthlvFKkILw7IS6sa+W/xcehdmb/0n97tJjvik2Rd9FmxKc7Pdj7JsCIXWrOZoM5EXVv319EaXSG1Svfh8fuiHWLJep+6kl5//XVsNlvnz8lkkrfffhu3291lv9NOO61fghNCiP4WjcYAMGYZ0WZ1fEA8+sZmklodPz97qoqRCaGeFdtaWJ4znW9+7fM8E1kL8rDHYzT28MVRiP2p90bQ6nRohkAlar3LTXFoDXrz0EiKRP9J+NoB0Dvs6CwWrPEQvlD3JdpC6LGa+nZs3Z4569qh0efc63eaoqIi/vWvf3XZ5vF4+M9//tNlm0ajkWRdCJGyIl/MTTeZDGiyOqr5lriMfLClDUVR0Gg0aoYnhCqa28M4EiG0ZovaoQw4Q34B9sqgVLgWB6Q5pJBnGBo1TgxuN4e3rmP4hDPVDkWkmdfXtbKx8Gh+nZ2NRm/AkozQGOla9T0aSxDX6rFk9e1715fD4DP/hhn0IVlftGjRQMYhhBCDIhLtuLNrNOhJmDt61kuyNfiCMVp9EdwZvhyPED1pCSZw6OJD4maVMT+f7M01NHslWRd91xrXMS5riPTouVwoQHtDE8bhpWi1mf/+IPpHnTdKTG9EazACYNMm8Ee7rroT9HUUobNa+rYKial0BK5vfBNjcXH/BJvihsa7jRBCfCFbm6AsXIdep8GQl4/GaCSnsQKAinpZb10MTa1RDS6j2lEMDkN+AXObVnH+4QVqhyLSTCgSJ4ieXHsfx+2mKV22nd2WAn65qF1qPIg+aQslcGi/HPZ+CC2cV9D1O5YmGmF26zoKXH0b0aU1Gsn91rfRGjJ7qdE9JFkXQgwp420xzvctQ6PRoDObyZ4+E5a9T44ji0AP86mEyHSKopCv+CnOHhq9Zsb8AjyxdlyhVrVDEWnGoNdwYe0bjC/K/OkiABqtFret4y6eFJkTfdEWBafhy570HJNCuaa9yz5mJcaC5pUU5GT2kqEHS5J1IcSQEo7EiOq/HOruOPIo4o2N/Ha+g7mTC1WMTAh1aDQazm79mKn5Q6Nr3ZCTQ4vJyeNL6/CHYmqHI9KIJhikINCAMy/zl23bw2U3o0GhRdZaF72kKArepB6X5cvZ1mGznbebzTS1hTq3edsCVGXlgUmmH+6LJOtCiCHlzRod9zuO7Pw5a9QojIVFeD9YjKIo+3imEJkpHIkR9PoxDIGlqKBjuZ+kO4dPGjU0fuWLoxD7s/LzSt73HIphCKyasIfJ48JGTAoyil5TgO8EVzAp58s0M5llZlHIw+6mQOe2jbvbeXzYCST0Q2NayYGSZF0IMaTE4kkMfJmUazQaHPPm88mWZq648wOSSUnYxdCyYm0Vd4w4m0S2U+1QBk2Oo6O4pBSZE32xsaKNrdZh6N1Dp2dd73JjjwdkGLzoNa1GQ1HzLnJy7J3b7FYjoOD1fzlCIxCMoFGSmO0yDH5fJFkXQgwpkbiCXtN1+RD7nMOxxoIEIwkavdLTJoaWxkYv5kQYa+7QSUCy3U70SoIWnwztFb3X6A3jiAfRZWerHcqgMbjdfLv6Hb53/Bi1QxFpYkdFI+/ZxqPYHZ3bDBYLViWK1x/t3BYKxchKRtF9sTKP6Jkk60KIISWaUDBouvae67KzKbB0FNeqaQz09DQhMlZzSwB7LIDeOTSGwQMYPG4cCVlrXfRNSzCOSxdHox06X5/1OTlkxUIo7e3731kIYOuOBpY7D8H4lalVWosFWyJMW+DLZD0QiWNKxtDoh8Z66Qdq6LzbCCEEEEsoGDXdh7q7891kkaCmWZJ1MbQ0+6LYEwF0dvv+d84QBo+HQ1s3MLpo6PSQioOjKAotUS1u89BYNWEPgyeHHZYibn9+g9R1Eb3S3OLHFg9i/MoNYJ3Zwjh/BSMKvnzP1cZjuJKyJOD+SLIuhBhSzrRUc665stt2U1EhufF2qpskWRdDS1sogVOXGFK9hQZPDtO9m7sUQBJiX5KKwhylmtH2oZase4hrdGxtiuILyuoJYv9avCHs8QB6p7Nzm9ZiZk7TauZNzO/cdpyjne+EV6oQYXqRTykhxJCij0exGHXdthsLizixZjHnHjVShaiEUM9PHLs4xlivdhiDSu9249OZWbOhWu1QRJrQabXMbVnLiIKhVQxLm2XGoe+o89Lik2kjYv9aA3HsSgSt6csq71qzhbDWQEVVc+e2ZCSMNkvmq++PJOtCiCHllWA+H8dyum03FhTiirZj9LWoEJUQKmprw+oaOkPgoaPC9U5LEfctbSUWT+7/CWLIa2j2szWShdY5dJZt28PTuXqCFGQU+zfO6GestmuNA53Vyvrskdzy9LrO6RQP1blYZBqtRohpRZJ1IcSQsiNhpVHJ6rbdWFhEm97GfW9sp6ktRDAcJxiOs25nM4tW7lYhUiEGXnWjnwfDI2m3db+Blcm0RiNOY8f/t/olARH79+maKl4oOBKjZ+gl63a3HT1J6VkXvXKEUs3U7K7vq3qXG1s8RCIJ/lDHdIqWuB5Fb1AjxLQi5feEEENKTNFg1HUvkqPLzsZgNrG2Ic6/39rMrlofU0fnYLcYWbahnoWHDlMhWiEGVn1riEqdC7Nz6H0Jd2d3DNFs8YbJc8pQTLFvjU3tOGJ+DJ6hdWMLwJiTw0lN65gwYo7aoYgUF4kl2NGWoCSn6+oiercLW6KjmJzXHyXbYiSsaLEYhlYNiAMhybroRlEUXvukgs82N6LXaRlZZOeco2WYisgMcUWLofuUdTQaDe48Fw5tjMo6H3MnFbJwejHrdrTQ4gsTTyTR62QwksgsdQ1ejMkYztyhs2zbHm6XBXzQLMu3iV5obAvhjPkxuIdez7rB42F8/XsUeixqhyJSXE1TgH8xkSuzWin9ynatwYjd1PHlqy0QoVixElL0mHuoISS6km+eogtFUXh28XaeW7yDPJeZHEcW40qH3pc4kbmiaDHoer6Tayos4oeRT7n9J0dw1sJR5DjM2EOtKAq0+GSorMg8NXVe3FHvkExALB43hQlZO1r0TnMgjpPIkCyIpffkUKVz8uGnO9QORaS4li9ufro93eugOB1m9CQJRxK0+aMoGg32LEnW90eS9SEmnkiyubIV6Jgz8tO7P2TpurrOx0OROJ9tauSco0fzw1MnctkpE5g6KoekovDMe9t4/oPtbKlqUyl6IQ7egtBmxjt6XivWWFiItm43Oq2GZCRCw38fI/rYPwBobgsNZphCDIq6liDuWDt619C7KWtwe7iw+jUOn1igdigiDdiIUWCMqx2GKgw5Oey0FPHCJ1K/Rexbc1M7umQCZ46j22NZbhc3mNYyY1we2RYD32tZzGi3JOv7I8Pgh5DtNV6eencblfU+/vyjw9FqNYwtcfLQqxsxm/SMK3ViyTLwu4tnYfrasBSvP0pDW4gtVW288nEFV317CpPLPSr9JkIcuKn+7bjtPU/rMBYWokQiRCp2UffwQ8QaG8gtKGBSrBpL1sxBjlSIgXd8YQL/2s1d1sMdKvQeN0o0Stzvx5CdrXY4IsVdrN8Mzp5v9GY6g9uDPR6gPZSQKWFin5qafWTHAxjcZd0eM7jdBDdvBkCv01Lkr8Vq7V7wV3QlyfoQ8PG6Wj5YU8uWqjby3RauOXcadmtHGdwfnDqB+15czz3Pr6Uk18avvzsDo0FL61tvEvh8LTqHHXP5aJxHLeAnp09CURT+8tRq/vPWZm6+ZDamnib/CpGikorCWkMhU5IGnD08biwoAmD3X/6MxmRi+PW/JbhhAyc9+xTD878zqLEKMRhGRBrwWZNoDUa1Qxl0BreHxe6p/PvxNfz+h3PVDkeksHgiSaylGfPIUWqHogqt1YqDKAodNR7yXTJ3XfRMiUTwxNp7vAGsd3l4OVGK441NaACXIZ+8LEnW90dujQ0Bmyrb0GrgJ6dP5JZLZjOq2EEyHEJJJtFptfzglEOYM6GAo6YVo9dpaHruGRqffgK0GmKNjTQ8/m8an3oCRVHQaDRccNxYFAXqmoNq/2pC9Ek0luAl92FUhHu+T2nIyUFjNKI1Wyj5xa8wFRVj8LhpVwxU7aof5GiFGFj1LUEWVcVRcovUDkUVereHrGSMZl+0c91fIXqyfFMDtxiPJOkamiMKNRoNufaOG3pNbVKQUezdSUUxvl27CJ29+zB4vdtNNAk7qr0sXlNDUJc1JFdX6CvpWc9Qyzc1sLGilfOPHc1F3xiHRtNRUEtRFFpee4WmF55DYzRiKhlO7rfP5pKTDgGg8ZmnaH3zdXLPPhfXsccD0PbeuzQ8/hiJgJ/smbPxlJVx6w8PQ6eVez0ivUQiHfMNjfqeR4RotFqG/ewaDLm56J0dc3j1bg+LPYcSeGc7N1wqc1tF5tiyu403IwUcXpRQOxRV6LKzsSdDRBId9VosWbLer+hZY7MfYzKGNWfoFWLcw+OyMjbixWiQ736iZ4qiEGttQ2fLRmvo/n6qd7mwJUKsbwwAUBxuwFQyfLDDTDtyxWWYeCLJlqo2Hn5tI8FwDK1G05mox1qaqfnb3TQ9/yyuY4/Hc8ppKNEo1Xf/lWh9PW2L3ulI1M85rzNRB3AuOJr8Cy8msGY1NXf/lZ3XXkO8pppAOMZDr2zAG4iq9esK0SeRUEdFd6Nx7/cpzaPHdCbq0DFU1hH30+yT17nILHXNAezxALbCPLVDUYVGq8Vt7XgvaGmX1R7E3jU0eHHE/OjdQ7NnHcCU4+HswGeMHuZUOxSRoupagtyw3kydq6THxw1uN9Z4R7FeszZJgU2PzmYbzBDTkvSspylFUXhreRUleTYOGdFxp/fp97bxzordxBNJCj0WvnfCOBJeL+HKXQRWr8b70Ydos7Iouvyn2KZOA8BxxDwq//h7dt/+J+JtrTiPOQ7XMcd1O59j7pHYj5hHrKmR6jvvoPGpJ7BceiXrdrZQ3/Y5P/v2FMwmeTmJ1BYNdyTc+0rWv05rs+FUwrRHFaKxBEap0yAyRG29F0/UiyF/otqhqMbjMEO8Yx7usDz50ih61tQWxBEfmmus72Hw5NDU8imNbSFynUNv+Tqxf7XNQSKKFs9eXh96pwtbsmMaRZ4SIGu49Kr3hmRXaWrNtmaeWrQNgHmTC/nOcWOYPjYXm9nAmGFOSgts+N56narnnwVA53CQc9oZOBcs7LJGqM5mo/inP6fqjzdjnTKV3LPO2es5NRoNxtw8cs86h5p77sS5YyM/OWMSf/7vKn5694eML3Vz1oJyinPlC49IUckEheFGrGZnr5+i0WjwmDsS9Ob2MIUe6wAFJ8TgqmsKUBz1YswbutM7nG47P65bySEj5qsdikhhTb4Yo2OBLqOuhhpDTg5vZ0+i/fm1/Obi2WqHI1JQTVOALCWGuyC3x8c1ej1luiBHuiMUbVtL1vhJgxxhepJkPQ3F4kmefHcr40tdzBqfx7qdLeh1WsqLHJQXdRR0CO/cQdOLz3/RU34sek9O53D4rzPm5VH2x9vQGI1oejEP3Tp5CpbxE2h85knKf3cLt/7gMD7b3Mia7U2EIkNz7qNITWu3NxEMxzlsQkcykmfV8b3dr1Ps7tsHRI7ThDkaxxeMUTh0R0GKDDPaliAvXI8hZ+gW+DF63LjWf45hL3UshAC4osSLt6YKjX7ofm025OThiPnZ3hZSOxSRomoa/XgirRgL934D2OWyckxgPcGW7ZhKvjmI0aWvtHrXOeecc1i1alW37ePGjeN///ufChGpZ+7kQqaNzqE418b8qcVdHktGo9Q99M+O4nFnntWrDxdtH5ZO0Gg05J59DhU330jj00+Sd953OHZmCcfO7HmOihBqUBSFO59ZC8DU0TlkGfUkoh1Lz/RU+GRfcjzZ/KLyY0pLjiMUidPYFmJ4vqzLLNLbSeZ6/JbI0E5APB4+1hSx+eOdnHh493WBhQAwtTfjdA3tUYOG3FycMT+BaJJQJC5TH0U3NQ1ePBEvxoJpe91H73bjX92Ry5mGlw5WaGktra60LVu2cNRRR3HiiSd22e7sYS2/TBRPJFm3o4Wpo3M46fARRKp3kwhq0Vk61rv0r12Db9knhHfuIN7SzPDf/G7AvoSZhpWQd855NDz+GFllZdjnHAFAfWuQ597fzkUnjpc3cqEqjUbDz8+awh1Pr+GT9fUcNa2Ytbu8/K38Am6LaehLB7nBk0Ng9Soi0QS/+ucn6LVabvvx4QMWuxADrc0foaq2lby8fLVDUZXe7aHe5Gb3lnpJ1kWPtlS18WKjizNdMbVDUZXOasWl6/gbNHnDlEiNB/E1l0w0UvfZGoz5Z+91H73LDYkEWqsV/RCuAdEXaZNNVVdXEwgEOOqoozj11FPVDmfQtbSHue2/q6hpCvD7S2fjSQaouOm3GFxuCn98OcGNG2h65ilMw0uxTJhA9vSZmIqK93/gg+A4aiHhnTup//cjxJqasE6agt6Vz9rtzfz1mTWcOLuUyeUetNqeh98LMdAmjvQwbXQOi1ZWM39qEZFoDDQaDH1cosng9vCieTLr71iMyaDjF+dNHqCIhRgcn6yv54XQKG4a0ah2KKoyuD3Y40G2SjV4sRcVdT62Je1YXbIiSK7TjBYFrz8iybroxtJaj9Og9LjG+h57ijRmDS/d6/Rc0VXaJOtbtmwBoLy8XOVIBl8snuDuZ9cSisb5zYUzKfRYqX3gsY51DC0WKn//O0gmcZ94Ep7TvzVoL36NRkPeBd8FFFrfeoPm/72A57Qz+MkZR/Dihzu5+7m1HD6xoHMN969SFEUuUjFgvIEotz+5iosWlnHUOBcfbG4jHE0QjXass24ym/p0PL3bTVTbkeBffsYkinKsxOJJDHpZ/VKkp/qWAK5oO6b8od6z7sYeC9AaSpBMKnJzWXRT2xLAFfNhzJGCJc4cJ9f7VzBi5NFqhyJSzLbdXl7ZnOTE/OJ9fr/f05su66v3Xtp809y6dSsAo0aNAiAQCKgZzqBRFIX7nltLVb2fn5w+idKCbMIVu/B9+gk5p55OyXW/xnXs8eRf9H1yzjhz0BNgrcFIwcWXUv7Xe3AuPIaW115hvEfHDd+bwenzythS1UYimezynBWbGvj+n94jGB7aQ8rEwPn481rqW0LEX3wC+7MP8OPTJ7Kt2svTq9owJmMYjMY+HU/v8XBs46dcPddFUY6VH/1lMRsrWgYoeiEGXl1DO65YO4b8oVsJHkBrNOI0JEkqHTf5hPi6ugYf7mgbBo8k68bcXJLNQ3s0jujZjhovG8JmrHk9V4LfQ+/6IlmXZdt6LW2S9c2bN2MymbjrrruYPn06hx56KPPmzePf//632qENqERSoT0Q5cITx1FWaEeJx2l85imMBYXYj5iL1mAk99tn4zhinqpxavR6PKeejkZvoPmljmJ/J8wezq0/nIPuaxXmP15XB8DuxqFxw0UMvlVbm5g8PBs2rCZSVUWkYhfFOVaOGZnF96pe7XOBOYPLjT0ZYpjSjnb7BjQotMiwWZHGGlpDuKM+jEO8Zx2gyKblhOxWGSkjelTfEsQdlRtbAIbcPN5WSnno5fVqhyJSTE1zAE/cR9Y+KsFDR80r++FzsU6QZdt6K22GwW/dupVIJEJ9fT1/+MMfCIVCPPPMM9xyyy20tbVx5ZVXHtBx9Sn+4azTafn1RbPw+cJ4166l7vH/EK2tpeSqn2Mw9S3hGGh6RzY5J59MwzNPk3PCCZgLCwG6VQ0NR+OMHubgkLLMKSyh02m7/Feoq7k9TJnZj8ZgQGex4Pv4Qwq/eyEnlpuofK0dvckIfbn29Ub0DifRqkral3+KzXMcrb5Qyr9/pCu5ngZWPJGEZAJ3wk9WXm6vluzsSaa0U26ODU9kF87svk2PSReZ0k5q+WZJEv2WKsyFBWgH8D0/HdopqyCPuEZLVU3bkP38S4d2UkNNow9PuIWs4in7fm3ozQy77LIBjyeT2iltkvWzzz6bRCLBd7/73c5tp5xyCueeey4PPPAA5557Lrm5+x568XVarQaXy9rfoQ6IyKoVVN5xJ/ZDxjP+/36ObWRqVq11nHkq3vfeZddNN5Jz5Fy2jTmCfy2q5N83Ht+xdFZSoaLez7ePHp02f/u+sNvNaocw5CUSSbz+CFmNW8g54nBMHje1r7/J2B9eQuyLdzyHx462j0Phzfm5tL63CBSFbEeQ9vZQRr6GU4lcTwPnxrIm2nwh3J6DX4Iw3duptaiQ5WsqyQ7EKB/mVDucAZPu7aSWWcZWWlx6PHnOQTlfKreTefQIHDE/n7dHcTotQ7r2UCq3kxoaWkJMjraTM7oMWwp9N8qEdkqbZP3888/vtk2r1XL22Wdz3XXXsWLFCr7xjW/06ZjJpEJ7e7C/QhwQOp0WXXM9W+/9O47Dj6Do0suIaTS0tqbuEPLh1/2a1vfeo3nx+2i2VhPSzuDDz6qYMS6PpKJw7bcnsHzJOn61qYH/O2/vazGmE51Oi91upr09RCKR3P8TxIBJJhX+b2EegQefxvKdn6N3Okg8+zxV73xArLEeAF8wRjLQt5oJGocTFAXPCd/AvipAXUN7Sl+H6Uyup4HXXrEbfU7eQb2GM6WdkjY7b2jKqF+ynQuOH6d2OP0uU9pJDZX1PlZs9TMtJ3/A3+/ToZ0UnRlnIkA0obBrdytOW2aORtmXdGinwZZUFE4criFry24iFgexFPhulOrtZLebe93rnzbJ+t54vij4EQweWNIdj6deA35VIhRi1x//hKmwkNzvfI9EQgEUtcPaJ022E/cpp6PPLyD+z39QPGsun26oZ+qoHADsW1ejLPuYz/OPwBeIZtR67IlEMuVfU0OBY90yjO5sjOWj0Wg0mEePofqf/+joFR87lqTS92vfMnEK6A04v3kKx7/1U4Z9c5y09QCT62lgvLGskvcD5Vw9Jtwvf990byet04U9Wk9jcyCtf4/9Sfd2UsNnmxp4M5zHYSN0g/a3S+120pBj1QFQ2xTA1sdlUDNJarfT4Jum1OOzQVJnIJlCf5dMaKe0GMhfU1PDN7/5Te66665uj+3YsQOAkpKSwQ5rUCQCAUw5OQy7/Mo+D9tVm3XyVDR6PVPMAVZsbsTrj/D28ireXd9MQaQZ6LhrvUdSUfj9v1ewcZdU2RYHbuvuNv63W4dpwuTOIXqe07+F46iFjLj+Bib96ZYDOq798CMouPhSdGYzVpMOjbe1P8MWYtDUNvnRxGNSMOsLBrcHR9xPY4v6vUEitdQ1B3FHvFKI8SvyXVYusFRSnJM6Q52FuirrfSzfHcKQJ58pAyEtkvXCwkK8Xi/PPPMMXq+3c7vX6+WRRx6huLiYQw89VMUIB44xJ4dJf7gZYx/n46cCndmMZfwhTK5ZhSVLz46adj5aV0t1a4ScqBe9RqGi7stkvarez46adl7+eJd6QYu0t223l9W6AsxfWT7EMmYs+edfgGXU6H6ZY9fgGcHfN2kJyPKDIg3VNXhxR9sx5kkCAqB3e3DF2mn0RVGU1B65JgZXXWM77qhXbmx9hSUvh5FtO7EM4V510dWKzQ28GcjB+EVhadG/0iJZ12g0/Pa3v6WxsZGzzjqLRx55hAceeIAzzjiD5uZmbrnlFvT6zBlKnUls02fAto388fwJTChzs7vBT35rFTq9jgICVHylZ33tjo7e9p+dNVWlaEUmaGr2kR0LoPfkDNg5NHYHOyImmr3hATuHEAOloS2MK+aTBOQLuuxs8uLtlFgUwtGE2uGIFKEoCnWtYdyxdowFcq3sYcjNZbXfxHsrd6sdikgRtU0B3JE2TEVFaoeSkdIiWQc49thjue+++3C5XNxxxx3cf//9jBgxgscff5w5c+aoHZ7YC9uUjgJykc/XsGV3G0kFCsNN2GbMZEHjCr4xu7Rz33U7mpk2OkfWuhUHpbnFhz0exJAzcMm654tKpy0+WWtdpJdINIE3ouBWguidTrXDSQkarZbRljiX5TVmVA0VcXDiiSRjshMUxdvQuzJnqdmDZcjNY5fOzeJVkqyLDnWNPtxRL8ZCSdYHQlp9Ki1cuJCFCxeqHYboA112Nuax4/CvWM6ixo4Pu3xdmOxZsyn+5E7yNEHAhqIoTCxzs2lbPQ++tI5LTpmobuAibbW0R/DEAxgGsGfdleNAU5ekVZJ1kWYMei0/zavFGIkN6WWXvk7vdhNqaiEUiUvCLgAw6HWcY60hZO+4oSM6GPLycMd8bGoNoSiKvI8McUlFocEbYWy0HWNRsdrhZCR59xEDznHEPIIb13PRNCtXZm3CWlZGVmnHOvEvLNrC68sq0Gg0nDgtD/fGZazfWq9yxCKdTc2OMDregNY6cMVvjG432fEQzSmwPIkQfaHVanC37MaR51E7lJRiyMnlrrZSXlm6S+1QRIpoD0ZpqWuWIfBfY8wvwBVrJxJXaA9E1Q5HqCwSTTDGGqNIG0SXna12OBlJknUx4LJnH4axoBDvyy+SXbGJrJEj0Tsc6F0uIm1enl+8g9eXVbD9kzVYowF8sY47dUIciCN09RxiHdheQ4PbzYLmFUzJlwI7Ir18urGeN30ujDJfvQtjYSGOsJf6lgNbBlZknvdXVvP36Fi5Vr5GazKRa+lIH+pbQypHI9RmNuk5z7CT0TkmGWUxQCRZFwNOo9XiOeU0gus+J+FrJ6usHADT8FLm+zcwPD+bZ97bzpK1NdgSIZJo8Aelyrbou2A4zub6MIpnYFdP0LvdjPdXUKyTAnMivazb3sgOjRODLEXVhbGwCFfUS32TX+1QRIqob/bjjLTLtdKDvDwHs/RNWLNkyshQ1x6I4q2txyjF5QaMJOtiUNhmzMRYPAyArJEjO/5bOoJYVQWXnXwIec4sRlWtwhbvuEvb5pe5wKLvKup9PBIdRdCRN6Dn0Ttd1BtdLFnfyO4GP88t3k401vsq0rF4Aq+8xoUKGpt8OGN+jLIebhemwiJc0XYa2sIysksA0NDkwxn3S896D6yFBRzf+hnFuTa1QxEqe/njnTykmSzF5QaQJOtiUGi0WvLOvwDnwqPRZ9sBMJWOIOn34ww085ujXBR4qykeNYyT/GtwZptUjliko5b2jp7unDzXgJ5HazJR5RzOU1tj/OZfn/Lq0oouyxDuz7Pv7+Bn937Up5tS9a1BvDI/UBykJm8YR9yPoUB6C79K7/HgJkQsCV6/XGcCmrwRnDGfJCE9MBYW0dgSZFtls9qhCJXV1nmlEvwAk2RdDBrLmLHknXfBlz+PG4+xoJCa++6l/aMl6JxOcqdOZmLD52TL0CpxAJqavJgTYSx5A1cJfo+SrAQmjcK35o/kvp/PZ/QwZ6+fW/lFYt+XIYTX/eMT7nhqdR+jFOJLyaRCayiJUxNBZ5NCQF+l0WoZa4cbCypxyc3iIS+eSBKKJ3Hrk+gGsFhpujIWFvKJ8xD+/fomtUMRKmtoDeKK+aQS/ACSZF2oRmsyUfTTn5EMBWlf+hG2KVPRe9xssAxn06ZqtcMTaai5qZ3sAV5jfY+RLj2/zlrLN+eMwGTUEU8ke/1cbyDKMTOGYdD3/nkOq5FDxwzsXHyR2RLJJMfaWhlpQwoB9cBSWICmvkbtMEQK0Ou03GDdwDRXXO1QUpKxsBBXzEdDewRFpo0MWYqi0BZK4NBE0TudaoeTsSRZF6oy5uZRdPlP0WXbyZ49B4PLzVLXRJatr1U7NJGGtJEIeZHWAV1jfQ+9202spYVIVRVLH3yCy//6Qa/WXY/FkzS0BvEEm/l0Yz3XP7iMRHLfCXsyqeALxsi2GOSLkThgBr2OOe0bKClyqh1KSjIWFvJ8IJ83llWoHYpIAbG6WizFMrS3JzpbNh5tlGgC2mTayJAViiRIKuC0m+UG8ACSZF2ozlw+ipF33IVlzFj0Lje2eIg2n1TZFn13isfHKb6VaC2WAT+XweUmVl9H5R9vxvzp+0TjSTbsatnv8+KJJAuy28l+/Uns3noaWkNsrmzb53P8oRhJReE/b22ReevigFU3+tnQkpS5hXthLCzCi4mtu2Qe7lD3/soqHtRNwVAgQ3t7otFoyHd3fM42tMpyh0OVJUvP9fElTC6UqUMDSZJ1kRL23JHTWq1kKxFJSMQBiTY1offkDModXkNePko8Tvb0mbiGFVCoC7OxonW/zzOb9MxrW0tOzIvh+Ufx2E18trlxn88JhL9cyrCuWb4YiQPzyaoKXnPOkGR9L4yFRbhiPuqbZfm2oa6yspGI1oBJetb3Kj/fiVMJEenDSigisyiKQry+DnOBrJgwkCRZFylFo9GQbVBoj8hQX9E3oUic31QXsd1ZNijnsx06neHX/5b8iy/BNu1QhrftYsOulv0OU99R1cr2+iDOY45DCQYYH6tn5ZbGfS4XVeAy8387HkejJKltDvT3ryKGiMYGL46YXwoB7YUxPx9X3EejPy7Ltw1xDXuWOJS1o/fKUlTIj6r+x6Qyt9qhCJUsX1PJw56j0OVIPZ2BJMm6SDnDzArDNJKQiL5p9oaJo8XuGJx1XzVaLVkjytBoNNimTqPUtxuvP0qTd99TOF77cAuLHZPJnj6DnG+fzYitS/EGolTV7703L97Whi6ZwBXzUVOz/6H2QvSkqS2EIzk4BRjTkUavJ9esJZaEtl7UnxCZq8kXxUW4c6lZ0Z2xsBAlGiXaLJ9JQ1VNdTNthmxM+bIU6ECSZF2knMke+FZ0ndphiDTT1NYxPDy3cPDv8huHlTAyK8oNxTXkOs373LemKUBOzIupZDj2w4+gRBfkutJmSgv2vpTWouW7eKz4BNzRdmrq2vo5ejFUNAcTeEwaNFr56N+bslwzZ+l2YDbJ8qFDVVJRaIloyLHKa2BfjIVFLHZP4/fPrFc7FKGS1hYftngIY16e2qFkNPnEFilH53TT1hYgFpd5UKL3Gqob0SoJckoHf9iiRqPBMWUyyXWr9jkMPp5I0hhUyDeDNisLrcGIY84RaD/9gGQsttfn1jf5CelMHNe+mnM8+58XL8TXJRUFVzJIkdOgdigpzV2cz+i6dZKsD2UKnBdZw5R8uVb2xeDJwapN0OCTaSNDVWt7mGyiaLP23UkhDo4k6yLl+K1u7nQdy+bKVt5fVc3fX5RedrF/DXWt2ONBslSaj2ubMo31YSu/vm8JyWTPX1wa20Ik0FCU++VQfceR82kMwzX3LqGmqefpH15/BJsSoaC0EF1t5YDELzKbBjiv5m1mjJBhvftiKhnO2oSLFWvlOhuqNEqS4tqN5JVI0ax90Wi15DmziCkamTYyRHlDCexGtaPIfJKsi5TjynUC8OLibfi2bmXl5gYiUellF/s2P6uZs9qWorOrk4yYx47DZlCoa49RUe/rcZ9wKEpBpJmSsi+/BJqKivEML8QbSbK9pr3H57UHY2TrFEIFpTzW7JEic6LPwq1txINBqQS/H6aS4ayzl7NkZZXaoQiVbFi3i8X2CRikwvV+FRa6AKhvDakciVDDMfHtHO6UGzUDTZJ1kXLMuR4AdtQHSXzyAUkFdtR4VY5KpDp9Yw3FefZBWbatJ1qDgTETRmBU4qzf2bXgzo6adt79bDexxgYurHqV3NFdK9bnHTmPnEgr23Y29Hjs9hhkm7TYSorZbCigcrcU9BF9s+TTHfyl/Dz0BYVqh5LSDHl5uBIBGtok+Riq1m+pY619NKbiYWqHkvIKRhSiUZLUN/Z8o1lktqLG7YwocqgdRsaTZF2kHIPLzXjfTo5o38AhwUqyEhE2balROyyR4p5tyGaXu1zVGJyzD6MkWMf6TV1fr4+8sJLH397CyuVbQKPBNKyky+O2aYdSHGlmW0XPSfjJ4Q3M8cRxjyzFnAize1fdgP0OIjM1NrRhTYTJkqq9+6TRasm16WkKI/Nwh6j6Jh/OZBC906l2KCnPWjqCSypf4lBHTO1QxCDztwf4QFuCP1uWbRtokqyLlKO12Tit5RPmNayg8JIfUBxpZPO2erXDEiksGomxSl9EINujahyWceMpS7ayvSHUWSAxnkhS0x7n2MZlTP30eYzFw9CaTF2ep7NYKbVrqQ8phCLxbsctaN5JcYEDY0Ehnlg7tfUy0kT0TWNbCKcmikYvhdP2pyAnmzhamYc7RDX54nhMqDZKK50Yi4vwJPwka3arHYoYZHW7avnQM42Q1al2KBlPknWRcjQaDYa8fGwzZpI9YybHZHs5NrxZ7bBECmuo6OjJzi9SN1nXaLUcNi6XS1veRffF97zKnXUkNVoOOW4ew6/5BYWX/qDH504bX8D3697EqO3am9fe6uM90xi8ZmfHOtCGBPXt0osh+qY+kCTXLMlHb5SU5jLRt51ENKp2KEIFLXEduY4stcNIC1qDka3FU/jvmp7rtIjM1VLXBEDOMFm2baBJsi5S0rBrfkHB9y8DYMyU0di3rCQZkV4O0bO6io5h4fkj1J+PW3j4LBwtNYS3dNxgat9VQX64mZHTxmEZN36v8yDzpk0mx99AZNvWLtvrdjey1D2JqKVjXtiMXIU54e0D+0uIjBIPR2hSsigpkErwvVE4ajgn1X9Etr9Z7VDEIIuHgkz0bmXccKfaoaSNsKuA1YEsmTYyxDQ3eUFRcOar20kyFEiyLlKSPtuO1tCxxql18hQ+sE9gyaLV6gYlUlZjXceX6pxC9edOZY0sZ2PhFB58cxsARU07uSz8Kdl5Oft8nqlkOGvzJ/PM4o5EPFK9GyWZpLWhYx67u7Dj+eNG5jN692qUePfh8kL0JFqxkyt2Ps282erWdEgXpuJhtOut7N5SoXYoYpDFqqtZ0LySQyaWqh1K2sgvcBLX6Gj1SlHGoaS1LYiVKAa9Tu1QMp4k6yLlGfIL2G0v4dPNjWqHIlJUrq+eY6jEaFD/Q0Oj0WAYWc6akBW/P0Tjtl2YRo3u1fNC+aUsbzXw8gPP8/af/0HLa6/Q2uLrcvdaW1zCassIqrbKOtCid0Jbt2DOMuAskwSkN7QmE28Om8+LG2WJxKGmblsV1ZZ8WTWhD4pHdCxxV72zVuVIxGByBVuYYpTpD4NBknWR8jQaDWVuA1VBebmKnuU07ODoYrWj+NLUORNRNBpWvf8Zd2tnsNy2/2QdYPToQoIaIy+0OKnPK6ft3bfxtviwKFEMJiMA5pIS3sydzbr1UtBH9M67G9t4Y/hCNFp5D+2tHKuOppAM6x1qPtvRypOFx6D7YmSf2L+iMSPQKkl2S7I+pIxr3sypJUm1wxgS5JNbpIURJTn4tFm01DapHYpIMUoiwSYvNDtSJ1svGjcSVzLI+6uqiWn1jBjdu9gmHzGFqSYfP5mXy5kXHs9iQxnxHds4VPPlqBKjzYo7GaKmQda1FfunJBJs82sImJ1qh5JWCjxWmhUTcZluMqQ0esO49XGpBN8HJqeDE4LrGBaQZH2oSMZi1LdHibuluNxgkGRdpIXyiWUAbF+3TeVIRKqJ1tTwlnsGq2MOtUPpYkyOgZ2Gjjn0I8f1bvixxW7lyp+dyvQjJpGVl8dyzyRCipZveLoOx801Jan3SUV4sX+Rqkoa9XaGFTrVDiWtlJUXktDo2LV+x/+3d+dxUdbr4/9f98ywDTuyiqAIgQopaG65hFqp6amsjmamlWRlkmbZevJkJ1tt8WhaPzVxqyzXyqVO56MtapY/05OCCioqiAKy7zAz9/cPYnIcVDRhBriej4eP9H6/ubuG677ue9738r5tHYpoIqrJRF4l+LrJ6w2vVL8QZ7wy5a09rUX16Uw+DbqZneX29b2rpZLBumgWgjoEEV9+GLecDFuHIuxM2YnjFOv0+Lez/eRy5xsa35ngihy8lGrcXZ0u/wMXcHLQ0ivCm10+XSnzsPxsgZ5O5BocUGX2XXEZBYdSKXJwp324PIN7JSK6ReJirCTnqEwy11rU5OZQqNXj38bN1qE0O+XtwvmxwJmaKnndYWtQfDydMq0zvsH29b2rpZLBumgWNIrCLcHgekrO3ApLp9KyMClaQtt62zoUCx0iQ/H0diPEV3/V6xjULxKAT0osB1rXhfoQXJ5NdUEBJb/+wslXZlLy214ZvAsrJ9NqT3CGBskVkCuhd3PhWe0+OuTIMae1qDx1CgeTgeAQubX3SpW2CWabT3cyU+S1oq3BqeNnURUN7dt62TqUVkEG66LZKGl3Hb/maTDVyJlb8acTZ4tRUAkNsL+rIU9PG8Hj4/td9c+3D3BnYLcg/n5LZ4vlPW4I547sn6hKSyVn9ScYCgs5s3A+p+e+i7G09K+GLVoI1WRCn57CfW3LCGrjautwmh39dZGUpqXJSbBWovJoGpMqf2FwX3nF4ZXq2KX2UcXjR+Tux9bgZHYJGlTa+clxpSnIYF00G1n6QLb49qHwaLqtQxF2QjUYMOXlEu0Fzo7295yhoig4OV796+QUReHB4Z3p0sHHYrnOpw3lbj6c+HwtpspKQmfOwuPRqVSdPEnmu29hKJHJ50Tt8+rOJfnc2Ps6HHRyuL9Syfr2vOd7G+VnZOKs1qDsUAr6Tl1sHUaz5O7ughdVnMwqtHUoopGpBgNFxRUE65F3rDcROXqLZiO8cygAx2TCH/GHqqzTxBQdZfKQUFuH0qQUReHjwKF87xSObsht4OHFW7vLWR83juLicjLnvImxTN4R3dqVH0phT5vrSdf6XL6zsNI2MpRqjQPpv6fZOhTRyAyFhfxaque1vA4YjPI6qqvR1hVOl5rkTpQWrirrNP3z9vPs8BBbh9JqyGBdNBtBfu7oMHEiI9/WoQg7UZ6eTrHOFceQ1nfQ8HeB/Z5RLC8KQqvRMO6WSDKLaljX6W5KC0vJXfO5rUMUNpafcpjvvbtxprDK1qE0Sx1C/VBUlWPHzto6FNHIyg+lkK5vS2AbV3Ra+Wp8Nbp18CKgLBtDvnxHa8kq09NRFQWX0Ia95Ub8dbJHEs2GTqshyEUlI6+C4l07bR2OsAMnjp5mYYe7OZHf+gYjoZGhaBS475YoNBqFGzr5M+PeOPLKDKyLvJNzO3dRfijF1mEKGzHV1JCcVYYRDbHX+do6nGbJ0UFLoKOBU3mVtg5FNLKilEOku7YjrlOgrUNptgbdFE183j4qj8krdluy48fOMK/jGM6UGGwdSqshg3XRrPTs0ZHAAG/OLl1Mzqer5HarVu7E2VI0qIT42d/kco3ttr4deGpMLJEhXuZlIf5uPDUmlhLFidKIrmSvSMJU1fpOZAioPH6MVKe2tPN2xNfTxdbhNFvt/fRkmfRU5+TYOhTRSFRV5dCxXGoULd0i5MTW1dK6u1Mc2JEz/5OTxC3ZybPFVCoO+Ho42zqUVqNZDdazsrKYPn06ffr0oUePHkyZMoWMDJl5sjUZ2T+csYl30Wb0WM5t30ZFWqqtQxI2ohoMZJYrBOprr4C1Nn5eLlYTzwGEBXnw5mN96f7AaGry8ijevcsG0QlbK05J5rhrO7p3lver/xV/HxnL/Xk/ULxrh61DEY2kJjubkwZnfPUa2ra5+ldtCkjy6McP6WWYKitsHYpoBKrBQGapir8zf2nyXHFlms1gvbCwkAkTJvDzzz/zwAMP8Pjjj7N//37GjRtHvjwf06ooisK3SgfWdriN3P/8x9bhCBsp/H47Zxy96SDv+bSi02oweLVha8RwTv2639bhCBsoPXyEm13z6B0tt/X+Fd4+7vj27EHurl9QTTLxWEtUlnKQm4oO8NL4HiiKYutwmi1FUbixix973SI48/Ovtg5HNIKylINkO3rTPsDd1qG0Ks1msL5s2TIyMzNZsmQJkydPJiEhgaSkJM6dO8fixYttHZ5oYj2i/MnQ+fDlaQeqs2Xyn9bGUFRI7pcbKHPxIjxcBiP1Uzjl0Ib1pQFUFxTYOhjRhKoyMjAdS+WWnu3l/erXwMmwHszzHETWvoO2DkVcY6rJRMH2bbhGx+DhLQOQv+r2wZ1QNFo2/ZJp61BEI8jfvp0cJx86yveuJtVsBuubNm0iNjaWmJgY87LIyEj69OnDpk2bbBiZsIWoUG/uvyWC/Z6RfLtRJptrbc5+8TlarZb3JvdhQFe5zbc+emcdDw3vxCmXQL7dutfW4YgmdGDDVraHDKAs4npbh9IiRPfsjFYDG348ZutQxDV27pc9rFK6sEzfS+bAuQbc9Y7c3NGZPao/p9NO2ToccQ3VnMul4uDvvHiDA31jZLDelHS2DqAhioqKyMjIID4+3qotOjqanTt3kpOTg7+/f9MHJ2zmpu6hpO5JYUOBNx03fkunITeSlm+gstqIo4OGupvZ/L31tPF0pri8mjPn/nz3tKIoODloaR9Yezb9aGYRpgsO1u0D3HFy1JKdX05RWbVFm7e7E35eLpRXGjiTX457QSUlJZUYjSZ0Wg0d23oAkH6mmBqD5e2T7fxc0Ts7cK6wgvwSywnAPFwdCfTRU1Vj5OTZEos2RYHr2nkBcPJsCVU1Rov2wDZ6PPSOFJRUkVto+cyYq4sDwb6uGIwmjmcVW/0+w4M90Go0nM4tpazScpZPPy8XvN2dKC6r5mx+uUWbs6OW0D9uiUrNKLRab4dAdxwdtJzNL6f4gt+hj4cTvp4ulFXWcDrX8r3gDjoNYUG1v8NjWUUYjSol5dX8tjuFlLOBJI7shM5droRcSkyntvTU7eDrk950yizkunZeFJVWkV1guW24OOkI8XfDpKoczSyyWk9YkAcOOg1Z58ooraixaPP1dMbHw5nSihqyzlnm0NFBQ4fA2hzWV1+hAW44O+rILiinqPTPbUOr1dDBBE4aqKgykJFTavFzWq1CeFtPoP76CvZzxdXZgXNFFeQX19ZX3RdxD1dHgtq4Ul1jNNfB+VF1bu9tXm9FlcGiLdjXFS83J/KKKjmT/8dn/aODq4sDYUEeGIwmUk7U3cmgnrdeHxx0GtLPFFNcVm2x3ra+rvh7uVBQUsWJs8UWP+ripKPTHzHtS83FpFqut0sHH1ycdKSfKSavqJKs9NN8XRpGYBsdTs6OiL/O1dmBoe0UNmR6MG/1Xgbe0J7YCF/KKw2cPFsMioJGqT2maDUK4cF/bpvVF+yjg/3ccHOp3Tbzimpnma8rC3dXR4J9a7fNY/XsozuFeqEoinnbPF/bP7bN/OJKzlywj/Z0dcTb2xWjycTB9Dyr9UaFeOOg03DybAkl5ZbbZqCPHj8vF4pKqziZbVmHzo5a8wSXB47nYTJZ1ndkiBcuTjoyckrJK7acUd/fy4W2vq6UVtSQlllo0eag0xAT1gaAg+l51NSYLGKKaOeJh96R0+fKOJtn+Vl9PZ1pH+hORZWB5HTLRyQVpfauPICUE/kUl1WxYXs2xfoAHouPklvgr5HbRt5AzusrKPs+E2PYBJJPFqBRFJQ/6kSr1Zi3m/r23219Xa1qpI6bvrZGagxGjp223n9HhXqhURROni2hvNLyWBV0iRpxddbRIdADk0nl0EnrO9EiQ7xw0Gk4lV1CaaUBd7cySkorMRlNBDSwRg4ez7M6Bl7X7uI14uflQvAfNXLhcVmnU8w1kpyeb7WfCb9EjbTxdKJDoAcVVQZSTlg/Rnx+jZSf910w9cdf6erixnUD+6JxkmNLU2oWg/Xs7GwAAgICrNrqBuhnzpyRwXorNOG+/gQt+xJl6/cc/2YNX0WN4nCV5czHowdFMKx3KKmnClm40fI2xhB/N16Z2AuAOav3WR00/pXQi3Z+bmz95SQ//u+MRdvw3qH8fVAEGTklvPXpPos2LzdH3kvsD8CCDQfMA4Y6z9wbS+cOPvzwvyw2/3zSoq3/9UFMHNGZvKJK3vzkN4s2rUZh8bODAFi29TAnsy0H84/dEU2vzgHsOZzD6v9Ls2jrFt6GaX/vRkWVwWq9AB88OQC9s4bPtx/l4HHLHfi4WyIZ0qMdySfyWfy15UyvYUEezHzgBgDe+uQ3Lrw28cYjfQjw0fP1zhP8nGz5yMLfbuzAqIEdST9TzHuf/8+izdfTmbcn3wjA/LW/U1xee+D1ri7mei+VwH59rT6DsHZ3n2Ay/5tJ1vEsItp68L9jeSzbetiiT1SIF8+N647RqNa7bbzz+I34eDiz8afj/P9Hci3Xf1NHRvTtQFpGIfPXH7Boa+vryuyHewPw3hf7qay2/ELx8oM9aR/ozn9+zWD7vtMWbXcMDOfugWGcPldmFZO73oF/Tx0AwEdfHiS30PJLzlOjuxHTsQ07fj/DVztPWLT1jQ5g0t+iKSit4u3PLOsWYOnzgwH45LtUq5Nak0Z2oW9MIPuPnuOT7ywnt4wJ8+GpMbFU15iYu8ZyWwaYO7U/DjpHvt55gv1Hz1m03Ts4glt7hZKWWchHXyZbtIUGuDHrodp91IdfHsRgtKywVx/uTbCTju2/ZbLjQG199TBmMemR+3B0dLCKQ1ydoX/rQ9m7KziYGsQhBwOxEb6cyStjzur9Fv3cXByYN6122/z/vkom54ITY0/+vRtdw9uw68BZNu5It2jr0yWAR26PprCsmjn1bJtLnhuEAnz23zSOnrb88p4wojP9rg/if0fPsfI/lttmdJgPcV2CqK4xWe1nAd5/oj+eOke+2pnOvjTLbbPu+JmWWXTJ4+cH6w9c9Pj5f3szLnr8PJ1byvx1lvuN84+fy7Yervf46dHBh93JZy96/CwoqbKKt/b4Wfs98Yv/S+VUbjltDAaeGRFER5kF/ppx0bsw9pYoclYt5+dqDUtLQizaXZ11zH9yIACLvk4h+4KB87R7utItwpefD55lw0+WNdKrsz+P3RFDUWl1vfvvxc/Gg6Lw6X9TSbtggDvxts707xrE78fyWPHtEYu2zu29eWZsHDUGE+9+vt9qve8l9sPLzYmvdp7gt1TLY+DfB4UzvHd7jp4uYsEGy22unZ8r/0qoPQZ+sOEA1TWWNfLKxF6E+Lux7bdMftifZdE2rFcoowdHkHWujHnrfrdo83R15P0n/qyRCwf6T98bS3QHH35JyWbTrhMWbf1iAkkY2YXC0iqreBUFPn6u9hi49vtjnDjvgpFWdSIk9iY0Tk5Wvx/RuBS1Gdz3s2/fPu69915mzpzJ/fffb9G2Zs0aXnrpJZYtW0bfvlf25d1oNFFcbN8zVmq1Gjw8XCgursBolMltLsZQXEzhjz9w7ttvKKowojq5mAeNLqZqnNUaqhQdZVpni8GkVjXhZaw9UOTp3ADLM+uehjJ0mCjVOFOlsfzi62yqxtVURY2ipVirR1H+vEKiUU14G2uvvBVoXTEplk+cuBvLcVSNlGmcqNRYnqF0MtXgZqrEgIYinfXzpm0MtTvPQq0eo2I5G6ebsQIn1UCF4ki51nKH6qAa8DBWYEKhQGf9qjNvQ+1r0Iq1LtQolufx9MZKXP74HZZqLU+G6FQjnubfofWVbi9DGdqL/A5dTFXoTdVUK1pKtJaz8J7/O8zXuqEqCtqKMtrf1IfA+yegaK78KZ7WWE/GigpSpz2BWl0NWi3VjnrKrLYNIx7GClQgv94clqJFpUTjQrXGctuoy2F920Ztff2RQ50b6kXry4mqC+rARa1Bb6ykBi3FOsttQ1FVfIy1VzAuVV/lGkcqzluvAjiaDLiZKjGioei8bU75Y89Qt82VaFwwmtdb26Y3VeOoGqhSdBZ1q6CiVU24mqpQgVLN+b+H2p91NVWhQaVCcbRar6NqwFE1YkBjUSMKKoqq4qLWnqgqP///+ce+xkmtQYNKlRGMRiMaRSE8cQrucXE0ttZWT4biYjI/XEjZ4UNonZwwoKFUW3usUVFQFQUFFR9D7bZZqHXFeMHVWndjBY6qkQrFctuE2u2gdttUKNZaz0ruZSxDAUo0zlb7fhdTFU71bJsAWtWIu1qNSVXrXa+7sQINKuUaRwwXrNfJVIOTaqAG7QXrVdGg4mqqHUiXaqxf4+RiqkKLSqXigOGCGnVQjTipBoxoqLzguKCgojfV3mlTrrG+gudkMqDFRI2ixYBlvFpMOKoGTChUKdbXo+pqqaLKgKoo+MRE0+Gpp6z62UJLq6fiPXs4tWgRZTpn0GjNdQJ/7mfrr5FKHFUD5RpHq235z/33X6uRC2tPp5pwM1WiwiVrpEzjhFHRoCiK+W6ti9cIKJhwa3CNWMbroBoaXCN1v9e649j5NVJzwXp1asNqpErRoaJgMtSA0YiDoyORs/6JU2DzePTQ3uvJw8MFrbZh32ObxZX1uoK41C1KV3P7kkaj4O3dPCbf8fCQ9+RekrcrfuPvpePoUZzbsRNDaenlf+YCoY0Qlr2ut8O1CuICjf1ZHb298R3Q/y/frtiq6snbFde571J+KoPqggJMNdWX7N6+kcKwxzpoSRSNFn1oCK4dw3Bo4sdDWk09ebvi+9oscn/4iZoS69vULyTbpv1y9PbGLTwc56DAqzrx25haSj153xpPm4j2FB04cNE+UiP2T+PgiGtYB1w7tEfr0vy2zZZQT81isK7X157lqqiwvgpeWVl764ebm/WVwssxmVSKi8sv39GG7P3MkD1y7N6bpn6aRvLUdAoLr75mW22e9J5oOnlifV7fPrXaPP1FKlBqAArKLtf1mmiteXKI60lzesCgtebpciqByiL7ubuyRebJ2x/9wCG2juKaapF5ugwjUFxpgsqmObZcC/aepxZ3ZT04OBiA3Nxcq7acnByg/ufZG8JgsL8E1sdoNDWbWFszyVPzIHlqHiRPzYPkqXmQPDUPkqfmQfLUPLSEPNnXvT8X4e7uTmhoKMnJyVZtycnJBAYG4ufnZ4PIhBBCCCGEEEKIa69ZDNYBhg0bxt69ey0G7KmpqezevZuRI0faMDIhhBBCCCGEEOLaaha3wQMkJCSwceNGEhISSEhIQKPRkJSUREBAAAkJCbYOTwghhBBCCCGEuGaazZV1Ly8vPv30U7p3787ChQtZtGgRcXFxrFixAh8fH1uHJ4QQQgghhBBCXDPN5so6QEhICAsXLrR1GEIIIYQQQgghRKNqNlfWhRBCCCGEEEKI1kJRVVW1dRC2oqoqJpP9f3ytVmOX7wgUliRPzYPkqXmQPDUPkqfmQfLUPEiemgfJU/Ngz3nSaBQURWlQ31Y9WBdCCCGEEEIIIeyR3AYvhBBCCCGEEELYGRmsCyGEEEIIIYQQdkYG60IIIYQQQgghhJ2RwboQQgghhBBCCGFnZLAuhBBCCCGEEELYGRmsCyGEEEIIIYQQdkYG60IIIYQQQgghhJ2RwboQQgghhBBCCGFnZLAuhBBCCCGEEELYGRmsCyGEEEIIIYQQdkYG60IIIYQQQgghhJ2RwboQQgghhBBCCGFnZLAuhBBCCCGEEELYGRms27GsrCymT59Onz596NGjB1OmTCEjI8PWYbVq9957L1FRUVZ/7rjjDnOfgoIC/vnPf9K/f3/i4uJ48MEHSUlJsWHUrcOiRYvo169fvW2VlZW88847DBo0iG7dujFmzBh+/vlnq35Go5HFixdz66230rVrV26//Xa2bNnS2KG3KpfK03vvvVdvfUVFRVFcXGzuJ3lqPL///juTJk3ihhtu4Prrr+fOO+9k48aNFn2knmyrITmSWrIPR44c4ZFHHqF379707NmTqVOncvLkSYs+Uk+215A8SU3Zj9OnT9O9e3eef/55i+UttZZ0tg5A1K+wsJAJEyZQWlrKAw88gKOjI0uXLmXcuHFs3LgRHx8fW4fYKqWmphIfH89tt91msdzLywuA6upqHn30UY4cOcKDDz6Ir68vK1eu5P7772fdunWEhYXZIOqW74cffmDevHl4enrW2/7000+zfft27rvvPjp27MjatWt5+OGHWb58OTfccIO531tvvcXy5csZNWoUsbGxfPPNN0yfPh2TycTIkSOb6uO0WJfLU2pqKiEhITzxxBNWbS4uLua/S54ax7Fjxxg/fjyenp48/PDDuLq6smXLFp577jkKCgp46KGHAKknW2pojqSWbC89PZ2xY8fi6enJo48+itFoZPny5YwePZqNGzcSFBQESD3ZWkPzJDVlH1RV5cUXX6SsrMyqrcXWkirs0vvvv69GRUWpBw4cMC87cuSI2rlzZ/XNN9+0YWStV2ZmphoZGal++umnF+3zxRdfqJGRkep//vMf87KcnBy1R48e6hNPPNEUYbYqJpNJXblypRodHa1GRkaqN954o1WfXbt2qZGRkWpSUpJ5WVlZmTpkyBB11KhR5mXp6elqp06d1FdffdW8zGAwqGPGjFH79eunVlVVNepnackakidVVdVBgwapTz755CXXJXlqPJMmTVJjY2PVs2fPmpcZjUZ1zJgxamxsrFpaWir1ZGMNyZGqSi3Zg6lTp6pdu3ZVMzIyzMsOHz6sRkZGqrNnz1ZVVY5P9qAheVJVqSl7cf53ieeee868vCXXktwGb6c2bdpEbGwsMTEx5mWRkZH06dOHTZs22TCy1is1NRWA8PDwi/bZtGkT/v7+3HLLLeZlfn5+DB8+nG3bttV7JlBcvTFjxvDqq6/Su3dvoqOj6+3z9ddf4+DgwOjRo83L9Ho999xzD8nJyZw4cQKAzZs3YzKZGDdunLmfVqtl3Lhx5ObmsmfPnkb9LC1ZQ/JUWlpKVlbWJesLJE+NxWg0smfPHgYMGEBAQIB5uUajYfjw4ZSXl3Po0CGpJxtqaI6kluyDTqdjxIgRtGvXzrwsKioKLy8vDh8+DMjxyR40JE9SU/bh1KlTvPvuuyQmJlq1teRaksG6HSoqKiIjI8NioF4nOjqanJwccnJybBBZ65aWlgZAREQEQL0D7+Tk5HoHI9HR0dTU1JgH/OLayMrK4l//+hdLlizB1dW13j4HDx4kLCwMvV5vsbwuTwcPHjT/183NzepRhQv7iSvXkDwdPXoUVVXNX4YqKiowmUxW/SRPjUOj0fDVV1/x7LPPWrXl5+cDtV9opJ5sp6E5klqyD++++y6vv/66xbIzZ85QWFhI27ZtATk+2YOG5ElqyvZMJhPPP/88UVFRPPDAA1btLbmWZLBuh7KzswEszpzX8ff3B2p3JKJpHTlyBCcnJ/7973/To0cPunfvzoABA1ixYgVQO3gvKSkhMDDQ6mclb41j27ZtjBkzBkVRLtonOzv7kjnJysoy97tUzdX1E1euIXmqO5H1008/ER8fT2xsLD169GDWrFlUVFSY+0meGoeiKISEhFhcXQIoLy9n3bp16PV6unTpIvVkQw3NkdSS/cnLy+OHH37gkUceQa/XM3HiRECOT/bmYnmSmrK95cuXc/DgQV5//XU0Guvha0uuJZlgzg7VXbE9f8KKOs7OzkDtwVk0rbS0NKqqqsjOzub111+noqKCNWvW8Nprr1FYWMi9994LSN6akqOj42X7lJWVXTIndQfasrKyeq/6XthPXLmG5Knuy9CBAwdITEzEzc2NH374gc8++4xjx46xfPlyNBqN5KkJqarKSy+9RG5uLlOmTMHJyUnqyc7UlyOpJftz9913m0/Wz5gxg8jISECOT/bmYnmSmrKt48ePM3fuXKZNm0bHjh2pqqqy6tOSa0kG63ZIVVWAS16FulSbaBxjxozBaDQyYcIE87Lbb7+dsWPHsmjRIsaMGXPZdUje7M/5OZGas50BAwbg7u7OpEmTzLexDRs2DG9vbz7++GO+++47hg4dCkiemoKqqsyaNYvNmzfTq1cvJk+e3KCfk3pqOhfLkdSS/Zk+fTqOjo5s3bqVd955h8zMTF555ZXL/pzUU9O6WJ6kpmzHaDTywgsv0LlzZ/PbLq5Gc64luQ3eDtXtCOo7u1NZWQmAm5tbk8YkYNy4cRYDdah9hnDMmDHU1NSwa9cu4M8cnU/yZjt6vb5BOWloP9E4brrpJqZNm2b1vNl9990HwO7duwHJU1OoqalhxowZrF69mq5du/Lhhx/i4OAASD3Zi0vlSGrJ/txxxx0MHz6cefPmMXz4cFavXk1aWprUk525WJ6kpmxn6dKlHDx4kGeeeYbCwkLy8/MpLCwEal+XnJ+fT3V1dYuuJRms26Hg4GAAcnNzrdrqJpar73kLYRtt2rQBaie/8PDwkLzZmbZt2zYoJw3tJ5pWXX3VPUIieWpcFRUVTJ48mU2bNtGrVy+SkpIsvrxIPdne5XJ0MVJL9mHEiBEApKSkSD3ZsfPzdDFSU43vxx9/xGAwcN9999G3b1/69u3LwIEDgdqZ3fv27cumTZtadC3JYN0Oubu7ExoaSnJyslVbcnIygYGB+Pn52SCy1isrK4sRI0bw73//26rt+PHjAISEhBAdHX3RvOl0Ojp37tzosQpL0dHRHD161OpMal2err/+enO/ujcxXKqfaBwPPvigeTKf851fXyB5akw1NTUkJiby008/MWjQIJYsWWI1CJR6sq2G5EhqyfaKiooYOnQos2fPtmqrm5fI2dlZ6snGGponqSnbee6550hKSrL4s2jRIgD69+9PUlIS/fv3b9G1JIN1OzVs2DD27t1rMfBLTU1l9+7djBw50oaRtU5BQUEUFRWxZs0aioqKzMuLiopYtmwZwcHBdO/enWHDhpGVlcV///tfc5/c3Fy2bt3KLbfcgpOTky3Cb9WGDRtGdXU1q1evNi8rLy9n7dq1dO3aldDQUACGDh2Koijm2f2h9lmpTz75hICAAG644YYmj7018fLyYteuXezbt8+8zGQy8cEHH6DVarntttsAyVNjmjdvHjt27GDw4MHMnz+/3v2V1JNtNSRHUku25+npiYODA19//bXFVbzq6mpWrFiBXq+nd+/eUk821tA8SU3ZTkxMDDfeeKPFnz59+gDg5+fHjTfeiL+/f4uuJZlgzk4lJCSwceNGEhISSEhIQKPRkJSUREBAAAkJCbYOr9VRFIWXX36ZxMRERo8ezdixY6murubzzz8nLy+PxYsXo9PpuPvuu/n000+ZMWMGEydOxMfHhxUrVqAoClOnTrX1x2iVBgwYwIABA5gzZw5nzpwhLCyML774grNnz/Lmm2+a+4WHhzNmzBhWrFhBWVkZsbGxbNmyhX379vH++++bnwcVjWPGjBns3LmTSZMmMX78eHx8fPj222/Zs2cPTz75JB07dgQkT40lJyeHpKQkdDod/fv3Z8uWLVZ9+vbtK/VkQw3NkdSSfXjllVeYMGECY8eOZezYsWg0GtavX09aWhqzZ8/Gy8tL6skONCRPUlP2ryXXkqLWTT0u7E5GRgZvvPEGP//8M46OjvTq1Ytnn33WfLuNaHrbtm1j0aJFpKSkoNPpiIuLY+rUqXTr1s3cJy8vj7fffpvt27djNBrp1q0bzzzzjNwC38jGjx/P8ePH2blzp1VbWVkZ77//Plu2bKGiooKoqCimT59O7969LfoZDAY+/PBD1q1bR0FBAWFhYUyePNk8y6v46y6Vp7S0NObOncsvv/xCdXU1ERERTJgwgTvvvNOin+Tp2vvmm2+YNm3aJfssXryYgQMHSj3ZyJXkSGrJPuzZs4f58+fz+++/A7VXCR999FEGDBhg7iP1ZHsNyZPUlP2oqqqia9eujBo1ymIg3lJrSQbrQgghhBBCCCGEnZFn1oUQQgghhBBCCDsjg3UhhBBCCCGEEMLOyGBdCCGEEEIIIYSwMzJYF0IIIYQQQggh7IwM1oUQQgghhBBCCDsjg3UhhBBCCCGEEMLOyGBdCCGEEEIIIYSwMzJYF0IIIYQQQggh7IwM1oUQQggbqKmpYf78+QwZMoSYmBji4+N54403KC0ttUk8UVFR/PLLLwAMHjyY9evX2ySOSzk/xss5dOgQv/32WyNHJIQQQjQena0DEEIIIVqjd955h127djF79mxCQkLIyMjgtdde4+TJk3z00Uc2jW3t2rXo9XqbxlCfHTt24Onp2aC+U6ZMITExke7duzdyVEIIIUTjkMG6EEIIYQMbNmzg9ddfp2/fvgC0a9eOWbNmMW7cOHJycvD397dZbD4+Pjb7f1+Kn5+frUMQQgghmozcBi+EEELYgKIo7N69G5PJZF4WFxfH5s2b8fb2BiA7O5upU6fSs2dPYmJiGDVqFHv37gUgMzOTqKgovv/+ewYPHkxcXByzZ88mNTWVu+66i9jYWB599FHzbfXPP/88s2fP5rHHHqNr167ceeedF71N/Pzb4MePH8+HH35IQkICXbt2ZejQofz000/mvgUFBSQmJhIXF8eQIUP47LPPiIqKqne969evZ+zYsbzzzjvExcURHx/PmjVrzO0mk4klS5YwZMgQunbtyvjx4zly5Ii5/cJb9T/55BNGjx7N9ddfzx133MHBgwfNMZ8+fZoXXniB559/HoD33nuP/v37m9eblpZ2BdkSQgghmp4M1oUQQggbmDBhAitXrmTw4MG8/PLLfPvtt1RWVhIREYGDgwMAM2bMwGg0snr1ajZu3EhAQACzZs2yWM+iRYtYuHAhr776KitXriQxMZGnn36ajz/+mP3797N27Vpz39WrVxMREcGGDRvo2bMnjzzyCPn5+ZeN9aOPPmLEiBFs2rSJTp06MXPmTPNJhqeeeor8/Hw+++wz/vnPf7JgwYJLruvAgQMcOnSIzz//nMTERF555RV27NgBwIIFC1i6dCkvvvgiGzZsIDg4mIcffpjy8vJ61zV//nweeeQRvvrqK9zd3Zk9e7Z5eWBgIC+++CL/+Mc/+O677/j888+ZO3cumzZtwtfXlxdeeOGyn1sIIYSwJRmsCyGEEDYwZcoU5syZQ2BgIF988QVTp05lwIABrFu3DgBVVbn55puZOXMm4eHhREREMG7cOI4ePWqxnscff5xOnToxcuRI2rRpw4gRI+jXrx89evSgb9++HD9+3Nw3IiKCGTNmEB4ezgsvvICnpydbtmy5bKw33XQTd911F6GhoUyePJkzZ86Qm5tLeno6u3bt4q233qJTp07cdNNNJCYmXnJdiqLw9ttvExkZyT333MOIESP44osvUFWVVatWMW3aNIYMGUJ4eDivvvoqWq2Wr776qt51jRo1iptvvpmwsDAeeugh85V1Ly8vtFot7u7uuLu7c/r0aRwcHGjbti2hoaHMnDnTfMVdCCGEsFfyzLoQQghhI7fffju33347BQUF7Nixg1WrVvGPf/yDqKgoYmJiGDt2LFu2bOG3334jPT2dgwcPWtw2DxASEmL+u7OzM8HBwRb/rq6uNv/7/MnWNBoNXbp04dixY5eNs0OHDua/u7m5AWAwGDhy5AheXl4WMcTGxl5yXe3bt6dNmzbmf8fExLB69Wry8vIoLCykW7du5jYHBwdiYmIuGuOFcdXU1NTbb8SIEaxatYohQ4YQGxvLzTffzD333HPJOIUQQghbkyvrQgghRBM7fPgwb775pvnf3t7e/O1vf2PlypUEBgaan2WfOHEiS5cupW3btiQkJPD2229brUur1Vr8W6O5+KFdp7M8R280Gi/Zv07dbfnnU1UVnU6HqqqX/fmGxODk5FRvf6PRaHWC4lJx1cfPz4+tW7fy4YcfEhkZyccff8zo0aOpqKi4otiFEEKIpiSDdSGEEKKJGY1GkpKSSElJsVju6OiIs7MzPj4+HD16lD179rBs2TIee+wx4uPjycnJAbjiAXKdQ4cOWcRw+PDhi04G1xDh4eEUFRWRkZFhXlZ3K/rFnDx5krKyMov+kZGRuLu74+vry/79+81tNTU1JCcnExYWdtUxAnz//fesWbOG+Ph4XnnlFb788ktOnDhBamrqX1qvEEII0ZjkNnghhBCiiUVHRxMfH8/jjz/O008/TVxcHOfOnWPDhg1UV1dz6623UlpaikajYfPmzQwePJgDBw4wf/58AItb26/Er7/+ytKlS4mPj2fVqlVUVFQwbNiwq/4cYWFh9O/f3zyRW15eHvPmzbvkz5SXl/Pyyy8zefJk9u7dyzfffMPy5csBePDBB5k3bx7+/v60b9+exYsXU1VVxW233XbFsen1eo4fP05hYSEmk4m3334bPz8/OnfuzObNm3FxcbG4jV4IIYSwNzJYF0IIIWxg7ty5fPTRR3zwwQdkZWWh1+vp378/q1atws3NDTc3N2bNmsWCBQt47733CAsL46WXXuK5554jJSXlqt45PnjwYHbv3s3cuXPp0qULSUlJeHh4/KXP8cYbbzBz5kxGjx5NQEAAd911F0uWLLlo/6CgIPz8/Ljnnnvw8/Njzpw59OjRA4CJEydSWlrKzJkzKS0tJS4ujpUrV17Ve9/rXhF34sQJPvjgA6ZOncobb7xBbm4uHTt2ZOHChXh6el715xZCCCEam6Je7b10QgghhGg26mY/P/9Z+b+qoqKCXbt2MXDgQPPz41u3bmXOnDls27bNqv/69ev54IMP6m0TQgghhCV5Zl0IIYQQV8XJyYkXX3yRBQsWkJGRwb59+1iwYAFDhw61dWhCCCFEsyeDdSGEEEJcFY1Gw4IFC9i1axcjR44kMTGRAQMGMH36dFuHJoQQQjR7chu8EEIIIYQQQghhZ+TKuhBCCCGEEEIIYWdksC6EEEIIIYQQQtgZGawLIYQQQgghhBB2RgbrQgghhBBCCCGEnZHBuhBCCCGEEEIIYWdksC6EEEIIIYQQQtgZGawLIYQQQgghhBB2RgbrQgghhBBCCCGEnZHBuhBCCCGEEEIIYWf+H/ctQ06W6LCnAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出测试集预测结果与真实值的对比折线图\n",
    "plt.figure(figsize=(12, 3))\n",
    "# 创建一个图形窗口，设置图形窗口的大小为宽度 12，高度 3\n",
    "plt.plot(y_test[1100:1500], label='True', color='r', linewidth=1) # 绘制 y_test 数据的折线图，并设置标签为 'Actual'\n",
    "plt.plot(ypred_test[1100:1500], label='Predicted', color='b' , linewidth=1, linestyle=\"--\") # 绘制 y_pred 数据的折线图，并设置标签为 'Predicted'\n",
    "plt.title('LSTM-CNN Prediction', size=10)  # 设置图形的标题为'DBO-LSTM Prediction'，字体大小为10\n",
    "plt.ylabel('PV/MW', fontsize=10)  # 设置y轴标签为'PV（kWh）'，字体大小为10\n",
    "plt.xlabel('Sampling points', fontsize=10)  # 设置x轴标签为'Sampling points'，字体大小为10\n",
    "plt.legend()\n",
    "plt.savefig(\"LSTM-CNN.png\")\n",
    "# 显示图例\n",
    "plt.show()\n",
    "\n",
    "# 显示图形窗口"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dde0cfbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xlrd\n",
    "import xlwt  #对xls文件进行改写\n",
    "from xlutils.copy import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1367737b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 新建表格\n",
    "def excel_create(path, sheet_name):\n",
    "    workbook = xlwt.Workbook(encoding = 'utf-8',style_compression = 0)  #相当于创建了一个EXCEL文件，style_compression:表示是否压缩，不常用)\n",
    "    workbook.add_sheet(sheet_name)  # 在工作簿中新建一个表格\n",
    "    workbook.save(path)  # 保存工作簿\n",
    "    \n",
    "# 写入表头\n",
    "def excel_write_title(path, title):\n",
    "    workbook = xlrd.open_workbook(path)  # 打开工作簿\n",
    "    new_workbook = copy(workbook)  # 拷贝原工作簿\n",
    "    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格，即index=0\n",
    "    for j in range(0, len(title)):\n",
    "        new_worksheet.write(0, j, str(title[j]))  # 在表格中第一行（row=0)写入标题\n",
    "    new_workbook.save(path)  # 保存工作簿\n",
    " \n",
    "# 向表格按列写入一维数组（列表）\n",
    "def excel_write_array(path, value, column):\n",
    "    workbook = xlrd.open_workbook(path)  # 打开工作簿\n",
    "    new_workbook = copy(workbook)  # 拷贝原工作簿\n",
    "    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格\n",
    "    for i in range(0, len(value)):\n",
    "        new_worksheet.write(i+1, column, float(value[i]))# 从第二行开始(row=i+1)，按对应的列(column)向表格中写入数据\n",
    "    new_workbook.save(path)  # 保存工作簿\n",
    " \n",
    "# 向表格写入二维数组（列表）\n",
    "def excel_write_array2(path, value):\n",
    "    workbook = xlrd.open_workbook(path)  # 打开工作簿\n",
    "    new_workbook = copy(workbook)  # 拷贝原工作簿\n",
    "    new_worksheet = new_workbook.get_sheet(0)  # 获取转化后工作簿中的第一个表格\n",
    "    for i in range(0, len(value)):\n",
    "        for j in range(len(value[0])):\n",
    "            new_worksheet.write(i+1, j, float(value[i][j]))# 从第二行开始(row=i+1)，按对应的行和列向表格中写入数据\n",
    "    new_workbook.save(path)  # 保存工作簿"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "43169c46",
   "metadata": {},
   "outputs": [],
   "source": [
    "path       = r'step2-CNN-BiLSTM.xls' \n",
    "sheet_name = 'NOx'\n",
    "title      = ['CNN-BiLSTM','y_test'] #表头\n",
    " \n",
    "excel_create(path, sheet_name) #新建表格\n",
    "excel_write_title(path, title) #写入表头\n",
    "#写入需要的一维数组(lat,lon,nox是之前已有的一维数组）\n",
    "excel_write_array(path, ypred_test,0)\n",
    "excel_write_array(path, y_test,1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79a18f6d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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